Overview

Dataset statistics

Number of variables46
Number of observations3376
Missing cells19952
Missing cells (%)12.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 MiB
Average record size in memory1.1 KiB

Variable types

NUM28
CAT16
BOOL1
UNSUPPORTED1

Warnings

DataYear has constant value "3376" Constant
City has constant value "3376" Constant
State has constant value "3376" Constant
PropertyName has a high cardinality: 3362 distinct values High cardinality
Address has a high cardinality: 3354 distinct values High cardinality
TaxParcelIdentificationNumber has a high cardinality: 3268 distinct values High cardinality
ListOfAllPropertyUseTypes has a high cardinality: 466 distinct values High cardinality
LargestPropertyUseType has a high cardinality: 56 distinct values High cardinality
YearsENERGYSTARCertified has a high cardinality: 65 distinct values High cardinality
PropertyGFABuilding(s) is highly correlated with PropertyGFATotal and 1 other fieldsHigh correlation
PropertyGFATotal is highly correlated with PropertyGFABuilding(s) and 1 other fieldsHigh correlation
LargestPropertyUseTypeGFA is highly correlated with PropertyGFATotal and 1 other fieldsHigh correlation
SiteEUIWN(kBtu/sf) is highly correlated with SiteEUI(kBtu/sf) and 2 other fieldsHigh correlation
SiteEUI(kBtu/sf) is highly correlated with SiteEUIWN(kBtu/sf) and 2 other fieldsHigh correlation
SourceEUI(kBtu/sf) is highly correlated with SiteEUI(kBtu/sf) and 2 other fieldsHigh correlation
SourceEUIWN(kBtu/sf) is highly correlated with SiteEUI(kBtu/sf) and 2 other fieldsHigh correlation
Electricity(kWh) is highly correlated with SiteEnergyUse(kBtu) and 1 other fieldsHigh correlation
SiteEnergyUse(kBtu) is highly correlated with Electricity(kWh) and 1 other fieldsHigh correlation
Electricity(kBtu) is highly correlated with SiteEnergyUse(kBtu) and 1 other fieldsHigh correlation
NaturalGas(kBtu) is highly correlated with NaturalGas(therms)High correlation
NaturalGas(therms) is highly correlated with NaturalGas(kBtu)High correlation
SecondLargestPropertyUseType has 1697 (50.3%) missing values Missing
SecondLargestPropertyUseTypeGFA has 1697 (50.3%) missing values Missing
ThirdLargestPropertyUseType has 2780 (82.3%) missing values Missing
ThirdLargestPropertyUseTypeGFA has 2780 (82.3%) missing values Missing
YearsENERGYSTARCertified has 3257 (96.5%) missing values Missing
ENERGYSTARScore has 843 (25.0%) missing values Missing
Comments has 3376 (100.0%) missing values Missing
Outlier has 3344 (99.1%) missing values Missing
NumberofBuildings is highly skewed (γ1 = 43.39499472) Skewed
PropertyGFATotal is highly skewed (γ1 = 24.12940742) Skewed
PropertyGFABuilding(s) is highly skewed (γ1 = 27.62439064) Skewed
LargestPropertyUseTypeGFA is highly skewed (γ1 = 30.09595071) Skewed
SiteEnergyUse(kBtu) is highly skewed (γ1 = 24.84197927) Skewed
SteamUse(kBtu) is highly skewed (γ1 = 26.72088824) Skewed
Electricity(kWh) is highly skewed (γ1 = 28.72846386) Skewed
Electricity(kBtu) is highly skewed (γ1 = 28.72846389) Skewed
NaturalGas(therms) is highly skewed (γ1 = 30.03889031) Skewed
NaturalGas(kBtu) is highly skewed (γ1 = 30.03889028) Skewed
PropertyName is uniformly distributed Uniform
Address is uniformly distributed Uniform
TaxParcelIdentificationNumber is uniformly distributed Uniform
OSEBuildingID has unique values Unique
Comments is an unsupported type, check if it needs cleaning or further analysis Unsupported
NumberofBuildings has 92 (2.7%) zeros Zeros
PropertyGFAParking has 2872 (85.1%) zeros Zeros
SecondLargestPropertyUseTypeGFA has 126 (3.7%) zeros Zeros
ThirdLargestPropertyUseTypeGFA has 48 (1.4%) zeros Zeros
SourceEUIWN(kBtu/sf) has 36 (1.1%) zeros Zeros
SteamUse(kBtu) has 3237 (95.9%) zeros Zeros
NaturalGas(therms) has 1258 (37.3%) zeros Zeros
NaturalGas(kBtu) has 1258 (37.3%) zeros Zeros

Reproduction

Analysis started2020-12-12 19:43:55.897788
Analysis finished2020-12-12 19:45:16.177371
Duration1 minute and 20.28 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

OSEBuildingID
Real number (ℝ≥0)

UNIQUE

Distinct3376
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21208.99111
Minimum1
Maximum50226
Zeros0
Zeros (%)0.0%
Memory size26.5 KiB
2020-12-12T14:45:16.231418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile275.5
Q119990.75
median23112
Q325994.25
95-th percentile49784.25
Maximum50226
Range50225
Interquartile range (IQR)6003.5

Descriptive statistics

Standard deviation12223.75701
Coefficient of variation (CV)0.5763478776
Kurtosis0.6508667434
Mean21208.99111
Median Absolute Deviation (MAD)3012.5
Skewness-0.008278915001
Sum71601554
Variance149420235.6
MonotocityNot monotonic
2020-12-12T14:45:16.313489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
246881< 0.1%
 
354361< 0.1%
 
6571< 0.1%
 
231821< 0.1%
 
6491< 0.1%
 
231741< 0.1%
 
497971< 0.1%
 
293151< 0.1%
 
231701< 0.1%
 
252171< 0.1%
 
272641< 0.1%
 
211191< 0.1%
 
231661< 0.1%
 
6371< 0.1%
 
272601< 0.1%
 
211151< 0.1%
 
231621< 0.1%
 
6331< 0.1%
 
237231< 0.1%
 
231581< 0.1%
 
6291< 0.1%
 
272521< 0.1%
 
3791< 0.1%
 
248761< 0.1%
 
211031< 0.1%
 
Other values (3351)335199.3%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
51< 0.1%
 
81< 0.1%
 
91< 0.1%
 
101< 0.1%
 
111< 0.1%
 
121< 0.1%
 
131< 0.1%
 
ValueCountFrequency (%) 
502261< 0.1%
 
502251< 0.1%
 
502241< 0.1%
 
502231< 0.1%
 
502221< 0.1%
 
502211< 0.1%
 
502201< 0.1%
 
502191< 0.1%
 
502121< 0.1%
 
502101< 0.1%
 

DataYear
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2016
3376 
ValueCountFrequency (%) 
20163376100.0%
 
2020-12-12T14:45:16.384050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:45:16.421082image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:16.459615image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters4
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
2337625.0%
 
0337625.0%
 
1337625.0%
 
6337625.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number13504100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
2337625.0%
 
0337625.0%
 
1337625.0%
 
6337625.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common13504100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
2337625.0%
 
0337625.0%
 
1337625.0%
 
6337625.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII13504100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
2337625.0%
 
0337625.0%
 
1337625.0%
 
6337625.0%
 

BuildingType
Categorical

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
NonResidential
1460 
Multifamily LR (1-4)
1018 
Multifamily MR (5-9)
580 
Multifamily HR (10+)
 
110
SPS-District K-12
 
98
Other values (3)
 
110
ValueCountFrequency (%) 
NonResidential146043.2%
 
Multifamily LR (1-4)101830.2%
 
Multifamily MR (5-9)58017.2%
 
Multifamily HR (10+)1103.3%
 
SPS-District K-12982.9%
 
Nonresidential COS852.5%
 
Campus240.7%
 
Nonresidential WA1< 0.1%
 
2020-12-12T14:45:16.522669image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T14:45:16.569709image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:16.643773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length20
Median length20
Mean length17.16735782
Min length6

Overview of Unicode Properties

Unique unicode characters40
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i670411.6%
 
l49628.6%
 
36006.2%
 
t34506.0%
 
a32785.7%
 
R31685.5%
 
n30925.3%
 
e30925.3%
 
M22883.9%
 
-17943.1%
 
u17323.0%
 
m17323.0%
 
f17082.9%
 
y17082.9%
 
(17082.9%
 
)17082.9%
 
s16682.9%
 
N15462.7%
 
o15462.7%
 
d15462.7%
 
112262.1%
 
L10181.8%
 
410181.8%
 
55801.0%
 
95801.0%
 
Other values (15)15052.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3652463.0%
 
Uppercase Letter890115.4%
 
Decimal Number36126.2%
 
Space Separator36006.2%
 
Dash Punctuation17943.1%
 
Open Punctuation17082.9%
 
Close Punctuation17082.9%
 
Math Symbol1100.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
R316835.6%
 
M228825.7%
 
N154617.4%
 
L101811.4%
 
S2813.2%
 
H1101.2%
 
C1091.2%
 
P981.1%
 
D981.1%
 
K981.1%
 
O851.0%
 
W1< 0.1%
 
A1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i670418.4%
 
l496213.6%
 
t34509.4%
 
a32789.0%
 
n30928.5%
 
e30928.5%
 
u17324.7%
 
m17324.7%
 
f17084.7%
 
y17084.7%
 
s16684.6%
 
o15464.2%
 
d15464.2%
 
r1840.5%
 
c980.3%
 
p240.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3600100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(1708100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1122633.9%
 
4101828.2%
 
558016.1%
 
958016.1%
 
01103.0%
 
2982.7%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-1794100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)1708100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+110100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin4542578.4%
 
Common1253221.6%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i670414.8%
 
l496210.9%
 
t34507.6%
 
a32787.2%
 
R31687.0%
 
n30926.8%
 
e30926.8%
 
M22885.0%
 
u17323.8%
 
m17323.8%
 
f17083.8%
 
y17083.8%
 
s16683.7%
 
N15463.4%
 
o15463.4%
 
d15463.4%
 
L10182.2%
 
S2810.6%
 
r1840.4%
 
H1100.2%
 
C1090.2%
 
P980.2%
 
D980.2%
 
c980.2%
 
K980.2%
 
Other values (4)1110.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
360028.7%
 
-179414.3%
 
(170813.6%
 
)170813.6%
 
112269.8%
 
410188.1%
 
55804.6%
 
95804.6%
 
01100.9%
 
+1100.9%
 
2980.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII57957100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i670411.6%
 
l49628.6%
 
36006.2%
 
t34506.0%
 
a32785.7%
 
R31685.5%
 
n30925.3%
 
e30925.3%
 
M22883.9%
 
-17943.1%
 
u17323.0%
 
m17323.0%
 
f17082.9%
 
y17082.9%
 
(17082.9%
 
)17082.9%
 
s16682.9%
 
N15462.7%
 
o15462.7%
 
d15462.7%
 
112262.1%
 
L10181.8%
 
410181.8%
 
55801.0%
 
95801.0%
 
Other values (15)15052.6%
 

PrimaryPropertyType
Categorical

Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
Low-Rise Multifamily
987 
Mid-Rise Multifamily
564 
Small- and Mid-Sized Office
293 
Other
256 
Warehouse
187 
Other values (19)
1089 
ValueCountFrequency (%) 
Low-Rise Multifamily98729.2%
 
Mid-Rise Multifamily56416.7%
 
Small- and Mid-Sized Office2938.7%
 
Other2567.6%
 
Warehouse1875.5%
 
Large Office1735.1%
 
K-12 School1394.1%
 
Mixed Use Property1333.9%
 
High-Rise Multifamily1053.1%
 
Retail Store912.7%
 
Hotel772.3%
 
Worship Facility712.1%
 
Distribution Center531.6%
 
Senior Care Community451.3%
 
Supermarket / Grocery Store401.2%
 
Medical Office391.2%
 
Self-Storage Facility280.8%
 
University250.7%
 
Residence Hall230.7%
 
Refrigerated Warehouse120.4%
 
Restaurant120.4%
 
Laboratory100.3%
 
Hospital100.3%
 
Office30.1%
 
2020-12-12T14:45:16.714334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:45:16.780891image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length27
Median length20
Mean length17.18927725
Min length5

Overview of Unicode Properties

Unique unicode characters43
Unique unicode categories6 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i760713.1%
 
e45357.8%
 
l44277.6%
 
36406.3%
 
a30455.2%
 
t27964.8%
 
f27124.7%
 
M26854.6%
 
-24094.2%
 
s21823.8%
 
o21173.6%
 
m20793.6%
 
y20083.5%
 
u20053.5%
 
R17943.1%
 
d16502.8%
 
r15612.7%
 
L11702.0%
 
S9971.7%
 
w9871.7%
 
c8481.5%
 
h7701.3%
 
O7641.3%
 
n5490.9%
 
g3180.5%
 
Other values (18)23764.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4300474.1%
 
Uppercase Letter866014.9%
 
Space Separator36406.3%
 
Dash Punctuation24094.2%
 
Decimal Number2780.5%
 
Other Punctuation400.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M268531.0%
 
R179420.7%
 
L117013.5%
 
S99711.5%
 
O7648.8%
 
W2703.1%
 
H2152.5%
 
U1581.8%
 
C1431.7%
 
K1391.6%
 
P1331.5%
 
F991.1%
 
D530.6%
 
G400.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i760717.7%
 
e453510.5%
 
l442710.3%
 
a30457.1%
 
t27966.5%
 
f27126.3%
 
s21825.1%
 
o21174.9%
 
m20794.8%
 
y20084.7%
 
u20054.7%
 
d16503.8%
 
r15613.6%
 
w9872.3%
 
c8482.0%
 
h7701.8%
 
n5491.3%
 
g3180.7%
 
z2930.7%
 
p2540.6%
 
x1330.3%
 
b630.1%
 
k400.1%
 
v250.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2409100.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
3640100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
113950.0%
 
213950.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/40100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5166489.0%
 
Common636711.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i760714.7%
 
e45358.8%
 
l44278.6%
 
a30455.9%
 
t27965.4%
 
f27125.2%
 
M26855.2%
 
s21824.2%
 
o21174.1%
 
m20794.0%
 
y20083.9%
 
u20053.9%
 
R17943.5%
 
d16503.2%
 
r15613.0%
 
L11702.3%
 
S9971.9%
 
w9871.9%
 
c8481.6%
 
h7701.5%
 
O7641.5%
 
n5491.1%
 
g3180.6%
 
z2930.6%
 
W2700.5%
 
Other values (13)14952.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
364057.2%
 
-240937.8%
 
11392.2%
 
21392.2%
 
/400.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII58031100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i760713.1%
 
e45357.8%
 
l44277.6%
 
36406.3%
 
a30455.2%
 
t27964.8%
 
f27124.7%
 
M26854.6%
 
-24094.2%
 
s21823.8%
 
o21173.6%
 
m20793.6%
 
y20083.5%
 
u20053.5%
 
R17943.1%
 
d16502.8%
 
r15612.7%
 
L11702.0%
 
S9971.7%
 
w9871.7%
 
c8481.5%
 
h7701.3%
 
O7641.3%
 
n5490.9%
 
g3180.5%
 
Other values (18)23764.1%
 

PropertyName
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3362
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
Northgate Plaza
 
3
South Park
 
2
Pine Building
 
2
Parkside
 
2
Central Park
 
2
Other values (3357)
3365 
ValueCountFrequency (%) 
Northgate Plaza30.1%
 
South Park20.1%
 
Pine Building20.1%
 
Parkside20.1%
 
Central Park20.1%
 
Crestview Apartments20.1%
 
Airport Way20.1%
 
Lakeview20.1%
 
Jefferson Court Apartments20.1%
 
Bayview Building20.1%
 
Fairview20.1%
 
Canal Building20.1%
 
Garden Court Apartments20.1%
 
Seattle North1< 0.1%
 
Camlin1< 0.1%
 
Prefontaine1< 0.1%
 
(ID22881) INTERBAY BUILDING1< 0.1%
 
Salal Credit Union1< 0.1%
 
LUMEN Commercial1< 0.1%
 
The View at Bitter Lake Apartments1< 0.1%
 
Westwood Heights East1< 0.1%
 
Lucknow/NW Loft Apartments1< 0.1%
 
Leschi Shores1< 0.1%
 
T102 1011 B/C1< 0.1%
 
Thunderbird Apartments1< 0.1%
 
Other values (3337)333798.8%
 
2020-12-12T14:45:16.867966image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3349 ?
Unique (%)99.2%
2020-12-12T14:45:16.963548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length72
Median length18
Mean length19.40017773
Min length2

Overview of Unicode Properties

Unique unicode characters76
Unique unicode categories10 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
64319.8%
 
e52328.0%
 
a39176.0%
 
t38575.9%
 
n38205.8%
 
i34005.2%
 
r33745.2%
 
o30844.7%
 
l25843.9%
 
s21483.3%
 
u14652.2%
 
d14572.2%
 
A13962.1%
 
m13722.1%
 
C13392.0%
 
S11781.8%
 
h10611.6%
 
c9101.4%
 
g8971.4%
 
p8561.3%
 
B7951.2%
 
E7231.1%
 
T7081.1%
 
L6831.0%
 
y6531.0%
 
Other values (51)1215518.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4213564.3%
 
Uppercase Letter1304719.9%
 
Space Separator64319.8%
 
Decimal Number28074.3%
 
Other Punctuation3590.5%
 
Dash Punctuation3000.5%
 
Open Punctuation2000.3%
 
Close Punctuation1990.3%
 
Connector Punctuation13< 0.1%
 
Math Symbol4< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A139610.7%
 
C133910.3%
 
S11789.0%
 
B7956.1%
 
E7235.5%
 
T7085.4%
 
L6835.2%
 
P6535.0%
 
M6224.8%
 
I5744.4%
 
H5694.4%
 
N5063.9%
 
O4983.8%
 
R4743.6%
 
D4573.5%
 
W4533.5%
 
F2772.1%
 
G2722.1%
 
U2552.0%
 
V2391.8%
 
K1421.1%
 
J900.7%
 
Q650.5%
 
Y630.5%
 
Z90.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e523212.4%
 
a39179.3%
 
t38579.2%
 
n38209.1%
 
i34008.1%
 
r33748.0%
 
o30847.3%
 
l25846.1%
 
s21485.1%
 
u14653.5%
 
d14573.5%
 
m13723.3%
 
h10612.5%
 
c9102.2%
 
g8972.1%
 
p8562.0%
 
y6531.5%
 
k4711.1%
 
v4271.0%
 
w4191.0%
 
f2870.7%
 
b2280.5%
 
x820.2%
 
z700.2%
 
q560.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6431100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
050317.9%
 
149817.7%
 
240014.3%
 
52779.9%
 
42498.9%
 
32368.4%
 
61766.3%
 
81706.1%
 
71696.0%
 
91294.6%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-300100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(200100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)199100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.9626.7%
 
&8222.8%
 
/5615.6%
 
,4412.3%
 
'4211.7%
 
#339.2%
 
:51.4%
 
@10.3%
 

Most frequent Connector Punctuation characters

ValueCountFrequency (%) 
_13100.0%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+4100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5518284.3%
 
Common1031315.7%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e52329.5%
 
a39177.1%
 
t38577.0%
 
n38206.9%
 
i34006.2%
 
r33746.1%
 
o30845.6%
 
l25844.7%
 
s21483.9%
 
u14652.7%
 
d14572.6%
 
A13962.5%
 
m13722.5%
 
C13392.4%
 
S11782.1%
 
h10611.9%
 
c9101.6%
 
g8971.6%
 
p8561.6%
 
B7951.4%
 
E7231.3%
 
T7081.3%
 
L6831.2%
 
y6531.2%
 
P6531.2%
 
Other values (27)762013.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
643162.4%
 
05034.9%
 
14984.8%
 
24003.9%
 
-3002.9%
 
52772.7%
 
42492.4%
 
32362.3%
 
(2001.9%
 
)1991.9%
 
61761.7%
 
81701.6%
 
71691.6%
 
91291.3%
 
.960.9%
 
&820.8%
 
/560.5%
 
,440.4%
 
'420.4%
 
#330.3%
 
_130.1%
 
:5< 0.1%
 
+4< 0.1%
 
@1< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII65495100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
64319.8%
 
e52328.0%
 
a39176.0%
 
t38575.9%
 
n38205.8%
 
i34005.2%
 
r33745.2%
 
o30844.7%
 
l25843.9%
 
s21483.3%
 
u14652.2%
 
d14572.2%
 
A13962.1%
 
m13722.1%
 
C13392.0%
 
S11781.8%
 
h10611.6%
 
c9101.4%
 
g8971.4%
 
p8561.3%
 
B7951.2%
 
E7231.1%
 
T7081.1%
 
L6831.0%
 
y6531.0%
 
Other values (51)1215518.6%
 

Address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3354
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
309 South Cloverdale Street
 
4
2203 Airport Way S
 
4
2600 SW Barton St
 
4
2309 S Jackson St
 
2
516 1st Ave W
 
2
Other values (3349)
3360 
ValueCountFrequency (%) 
309 South Cloverdale Street40.1%
 
2203 Airport Way S40.1%
 
2600 SW Barton St40.1%
 
2309 S Jackson St20.1%
 
516 1st Ave W20.1%
 
100 West Harrison20.1%
 
2600 SW Holden St20.1%
 
3613 4th Ave S20.1%
 
201 Thomas St.20.1%
 
1100 Olive Way20.1%
 
10510 5th Ave NE20.1%
 
2400 11th Ave East20.1%
 
14050 1st Ave NE20.1%
 
1227 NE 143rd Street20.1%
 
4636 East Marginal Way South20.1%
 
500 5TH AVE20.1%
 
3018 Western Ave.1< 0.1%
 
3230 SW Avalon Way1< 0.1%
 
316 2nd Ave S1< 0.1%
 
2100 First Avenue & 106 Lenora Street1< 0.1%
 
5050 1st Ave S1< 0.1%
 
5250 40th Ave NE1< 0.1%
 
605 First Avenue1< 0.1%
 
650 S. Lucile St1< 0.1%
 
6518 Ravenna Ave NE1< 0.1%
 
Other values (3329)332998.6%
 
2020-12-12T14:45:17.061633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3338 ?
Unique (%)98.9%
2020-12-12T14:45:18.608964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length41
Median length17
Mean length17.23992891
Min length8

Overview of Unicode Properties

Unique unicode characters70
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
952116.4%
 
e42237.3%
 
132865.6%
 
t29295.0%
 
028324.9%
 
A22994.0%
 
v19773.4%
 
219693.4%
 
S18743.2%
 
n16382.8%
 
r15562.7%
 
515162.6%
 
a14952.6%
 
313832.4%
 
413252.3%
 
E13142.3%
 
h12912.2%
 
N12702.2%
 
o11952.1%
 
W10681.8%
 
i9241.6%
 
68581.5%
 
l8241.4%
 
77741.3%
 
u7551.3%
 
Other values (45)810613.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2185937.6%
 
Decimal Number1509125.9%
 
Uppercase Letter1087718.7%
 
Space Separator952116.4%
 
Other Punctuation7991.4%
 
Dash Punctuation490.1%
 
Open Punctuation3< 0.1%
 
Close Punctuation3< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1328621.8%
 
0283218.8%
 
2196913.0%
 
5151610.0%
 
313839.2%
 
413258.8%
 
68585.7%
 
77745.1%
 
95863.9%
 
85623.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
9521100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A229921.1%
 
S187417.2%
 
E131412.1%
 
N127011.7%
 
W10689.8%
 
T3673.4%
 
R2842.6%
 
V2822.6%
 
M2252.1%
 
P1981.8%
 
B1931.8%
 
L1911.8%
 
H1861.7%
 
O1611.5%
 
C1571.4%
 
D1491.4%
 
I1071.0%
 
G1051.0%
 
Y1041.0%
 
F970.9%
 
U870.8%
 
J610.6%
 
K600.6%
 
Q360.3%
 
X2< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e422319.3%
 
t292913.4%
 
v19779.0%
 
n16387.5%
 
r15567.1%
 
a14956.8%
 
h12915.9%
 
o11955.5%
 
i9244.2%
 
l8243.8%
 
u7553.5%
 
s7403.4%
 
d5742.6%
 
y5492.5%
 
k2781.3%
 
c2251.0%
 
w1580.7%
 
m1500.7%
 
g1250.6%
 
p850.4%
 
f840.4%
 
b500.2%
 
x290.1%
 
q4< 0.1%
 
z1< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-49100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.75294.1%
 
,151.9%
 
/111.4%
 
#101.3%
 
&91.1%
 
:20.3%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(3100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)3100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3273656.2%
 
Common2546643.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
952137.4%
 
1328612.9%
 
0283211.1%
 
219697.7%
 
515166.0%
 
313835.4%
 
413255.2%
 
68583.4%
 
77743.0%
 
.7523.0%
 
95862.3%
 
85622.2%
 
-490.2%
 
,150.1%
 
/11< 0.1%
 
#10< 0.1%
 
&9< 0.1%
 
(3< 0.1%
 
)3< 0.1%
 
:2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e422312.9%
 
t29298.9%
 
A22997.0%
 
v19776.0%
 
S18745.7%
 
n16385.0%
 
r15564.8%
 
a14954.6%
 
E13144.0%
 
h12913.9%
 
N12703.9%
 
o11953.7%
 
W10683.3%
 
i9242.8%
 
l8242.5%
 
u7552.3%
 
s7402.3%
 
d5741.8%
 
y5491.7%
 
T3671.1%
 
R2840.9%
 
V2820.9%
 
k2780.8%
 
M2250.7%
 
c2250.7%
 
Other values (25)25807.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII58202100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
952116.4%
 
e42237.3%
 
132865.6%
 
t29295.0%
 
028324.9%
 
A22994.0%
 
v19773.4%
 
219693.4%
 
S18743.2%
 
n16382.8%
 
r15562.7%
 
515162.6%
 
a14952.6%
 
313832.4%
 
413252.3%
 
E13142.3%
 
h12912.2%
 
N12702.2%
 
o11952.1%
 
W10681.8%
 
i9241.6%
 
68581.5%
 
l8241.4%
 
77741.3%
 
u7551.3%
 
Other values (45)810613.9%
 

City
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
Seattle
3376 
ValueCountFrequency (%) 
Seattle3376100.0%
 
2020-12-12T14:45:18.676523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:45:18.714555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:18.753589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length7
Min length7

Overview of Unicode Properties

Unique unicode characters5
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
e675228.6%
 
t675228.6%
 
S337614.3%
 
a337614.3%
 
l337614.3%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2025685.7%
 
Uppercase Letter337614.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
S3376100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e675233.3%
 
t675233.3%
 
a337616.7%
 
l337616.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin23632100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e675228.6%
 
t675228.6%
 
S337614.3%
 
a337614.3%
 
l337614.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII23632100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
e675228.6%
 
t675228.6%
 
S337614.3%
 
a337614.3%
 
l337614.3%
 

State
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
WA
3376 
ValueCountFrequency (%) 
WA3376100.0%
 
2020-12-12T14:45:18.811639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:45:18.848671image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:18.887704image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
W337650.0%
 
A337650.0%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter6752100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
W337650.0%
 
A337650.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin6752100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
W337650.0%
 
A337650.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII6752100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
W337650.0%
 
A337650.0%
 

ZipCode
Real number (ℝ≥0)

Distinct55
Distinct (%)1.6%
Missing16
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean98116.94911
Minimum98006
Maximum98272
Zeros0
Zeros (%)0.0%
Memory size26.5 KiB
2020-12-12T14:45:18.952760image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum98006
5-th percentile98101
Q198105
median98115
Q398122
95-th percentile98144
Maximum98272
Range266
Interquartile range (IQR)17

Descriptive statistics

Standard deviation18.61520454
Coefficient of variation (CV)0.0001897246573
Kurtosis10.49296463
Mean98116.94911
Median Absolute Deviation (MAD)10
Skewness1.99966218
Sum329672949
Variance346.5258402
MonotocityNot monotonic
2020-12-12T14:45:19.032829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
981092948.7%
 
981042517.4%
 
981222437.2%
 
981012306.8%
 
981051915.7%
 
981211865.5%
 
981341865.5%
 
981021695.0%
 
981191674.9%
 
981031614.8%
 
981251524.5%
 
981081293.8%
 
981151293.8%
 
981331243.7%
 
981071013.0%
 
98144992.9%
 
98116932.8%
 
98118852.5%
 
98199702.1%
 
98112682.0%
 
98126641.9%
 
98106501.5%
 
98117310.9%
 
98136280.8%
 
98195100.3%
 
Other values (30)491.5%
 
(Missing)160.5%
 
ValueCountFrequency (%) 
980061< 0.1%
 
980111< 0.1%
 
980121< 0.1%
 
9801320.1%
 
980201< 0.1%
 
980281< 0.1%
 
980331< 0.1%
 
980401< 0.1%
 
980531< 0.1%
 
980701< 0.1%
 
ValueCountFrequency (%) 
982721< 0.1%
 
982041< 0.1%
 
98199702.1%
 
981981< 0.1%
 
98195100.3%
 
981911< 0.1%
 
981851< 0.1%
 
981811< 0.1%
 
9817840.1%
 
9817720.1%
 

TaxParcelIdentificationNumber
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3268
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
1625049001
 
8
3224049012
 
5
0925049346
 
5
0002400002
 
5
3624039009
 
4
Other values (3263)
3349 
ValueCountFrequency (%) 
162504900180.2%
 
322404901250.1%
 
092504934650.1%
 
000240000250.1%
 
362403900940.1%
 
766620324040.1%
 
863288000040.1%
 
322404900730.1%
 
016400022230.1%
 
795400000530.1%
 
503630060530.1%
 
639200104030.1%
 
022504907730.1%
 
198520000330.1%
 
880970004030.1%
 
357320025030.1%
 
093900030030.1%
 
292504908720.1%
 
890000055520.1%
 
283210019020.1%
 
445872000020.1%
 
766620007020.1%
 
737860026520.1%
 
212370025020.1%
 
766620707520.1%
 
Other values (3243)329597.6%
 
2020-12-12T14:45:19.125409image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3191 ?
Unique (%)94.5%
2020-12-12T14:45:19.202475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length25
Median length10
Mean length10.00503555
Min length9

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
01136833.7%
 
231689.4%
 
529468.7%
 
627138.0%
 
126918.0%
 
923777.0%
 
723637.0%
 
421646.4%
 
320716.1%
 
819095.7%
 
2< 0.1%
 
-2< 0.1%
 
a1< 0.1%
 
n1< 0.1%
 
d1< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number33770> 99.9%
 
Lowercase Letter3< 0.1%
 
Space Separator2< 0.1%
 
Dash Punctuation2< 0.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01136833.7%
 
231689.4%
 
529468.7%
 
627138.0%
 
126918.0%
 
923777.0%
 
723637.0%
 
421646.4%
 
320716.1%
 
819095.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a133.3%
 
n133.3%
 
d133.3%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-2100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common33774> 99.9%
 
Latin3< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
01136833.7%
 
231689.4%
 
529468.7%
 
627138.0%
 
126918.0%
 
923777.0%
 
723637.0%
 
421646.4%
 
320716.1%
 
819095.7%
 
2< 0.1%
 
-2< 0.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a133.3%
 
n133.3%
 
d133.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII33777100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
01136833.7%
 
231689.4%
 
529468.7%
 
627138.0%
 
126918.0%
 
923777.0%
 
723637.0%
 
421646.4%
 
320716.1%
 
819095.7%
 
2< 0.1%
 
-2< 0.1%
 
a1< 0.1%
 
n1< 0.1%
 
d1< 0.1%
 

CouncilDistrictCode
Real number (ℝ≥0)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.439277251
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size26.5 KiB
2020-12-12T14:45:19.258023image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q37
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.120625473
Coefficient of variation (CV)0.4776961097
Kurtosis-1.444891233
Mean4.439277251
Median Absolute Deviation (MAD)2
Skewness-0.07015381123
Sum14987
Variance4.497052396
MonotocityNot monotonic
2020-12-12T14:45:19.307565image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
7103730.7%
 
359617.7%
 
250915.1%
 
436710.9%
 
533810.0%
 
12828.4%
 
62477.3%
 
ValueCountFrequency (%) 
12828.4%
 
250915.1%
 
359617.7%
 
436710.9%
 
533810.0%
 
62477.3%
 
7103730.7%
 
ValueCountFrequency (%) 
7103730.7%
 
62477.3%
 
533810.0%
 
436710.9%
 
359617.7%
 
250915.1%
 
12828.4%
 

Neighborhood
Categorical

Distinct19
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
DOWNTOWN
573 
EAST
453 
MAGNOLIA / QUEEN ANNE
423 
GREATER DUWAMISH
375 
NORTHEAST
280 
Other values (14)
1272 
ValueCountFrequency (%) 
DOWNTOWN57317.0%
 
EAST45313.4%
 
MAGNOLIA / QUEEN ANNE42312.5%
 
GREATER DUWAMISH37511.1%
 
NORTHEAST2808.3%
 
LAKE UNION2517.4%
 
NORTHWEST2106.2%
 
SOUTHWEST1664.9%
 
NORTH1454.3%
 
BALLARD1263.7%
 
CENTRAL1073.2%
 
SOUTHEAST952.8%
 
DELRIDGE802.4%
 
North421.2%
 
Central270.8%
 
Northwest110.3%
 
Ballard70.2%
 
Delridge40.1%
 
DELRIDGE NEIGHBORHOODS1< 0.1%
 
2020-12-12T14:45:19.374623image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-12-12T14:45:19.443182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length9
Mean length10.11404028
Min length4

Overview of Unicode Properties

Unique unicode characters34
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
N413612.1%
 
E374411.0%
 
A345710.1%
 
T31559.2%
 
O27198.0%
 
W18975.6%
 
18965.6%
 
S18415.4%
 
R17005.0%
 
U13103.8%
 
H12733.7%
 
D12413.6%
 
I11313.3%
 
L11143.3%
 
G8802.6%
 
M7982.3%
 
/4231.2%
 
Q4231.2%
 
K2510.7%
 
C1340.4%
 
B1340.4%
 
t910.3%
 
r910.3%
 
o530.2%
 
h530.2%
 
Other values (9)2000.6%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter3133891.8%
 
Space Separator18965.6%
 
Lowercase Letter4881.4%
 
Other Punctuation4231.2%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
N413613.2%
 
E374411.9%
 
A345711.0%
 
T315510.1%
 
O27198.7%
 
W18976.1%
 
S18415.9%
 
R17005.4%
 
U13104.2%
 
H12734.1%
 
D12414.0%
 
I11313.6%
 
L11143.6%
 
G8802.8%
 
M7982.5%
 
Q4231.3%
 
K2510.8%
 
C1340.4%
 
B1340.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t9118.6%
 
r9118.6%
 
o5310.9%
 
h5310.9%
 
e469.4%
 
l459.2%
 
a418.4%
 
n275.5%
 
d112.3%
 
w112.3%
 
s112.3%
 
i40.8%
 
g40.8%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
1896100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/423100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3182693.2%
 
Common23196.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
N413613.0%
 
E374411.8%
 
A345710.9%
 
T31559.9%
 
O27198.5%
 
W18976.0%
 
S18415.8%
 
R17005.3%
 
U13104.1%
 
H12734.0%
 
D12413.9%
 
I11313.6%
 
L11143.5%
 
G8802.8%
 
M7982.5%
 
Q4231.3%
 
K2510.8%
 
C1340.4%
 
B1340.4%
 
t910.3%
 
r910.3%
 
o530.2%
 
h530.2%
 
e460.1%
 
l450.1%
 
Other values (7)1090.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
189681.8%
 
/42318.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII34145100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
N413612.1%
 
E374411.0%
 
A345710.1%
 
T31559.2%
 
O27198.0%
 
W18975.6%
 
18965.6%
 
S18415.4%
 
R17005.0%
 
U13103.8%
 
H12733.7%
 
D12413.6%
 
I11313.3%
 
L11143.3%
 
G8802.6%
 
M7982.3%
 
/4231.2%
 
Q4231.2%
 
K2510.7%
 
C1340.4%
 
B1340.4%
 
t910.3%
 
r910.3%
 
o530.2%
 
h530.2%
 
Other values (9)2000.6%
 

Latitude
Real number (ℝ≥0)

Distinct2876
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.62403312
Minimum47.49917
Maximum47.73387
Zeros0
Zeros (%)0.0%
Memory size26.5 KiB
2020-12-12T14:45:19.516245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum47.49917
5-th percentile47.5417125
Q147.59986
median47.618675
Q347.657115
95-th percentile47.713
Maximum47.73387
Range0.2347
Interquartile range (IQR)0.057255

Descriptive statistics

Standard deviation0.04775842495
Coefficient of variation (CV)0.001002821933
Kurtosis-0.1411603852
Mean47.62403312
Median Absolute Deviation (MAD)0.028385
Skewness0.1400447677
Sum160778.7358
Variance0.002280867154
MonotocityNot monotonic
2020-12-12T14:45:19.595813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
47.6624690.3%
 
47.6159870.2%
 
47.6220860.2%
 
47.5254950.1%
 
47.6154350.1%
 
47.6239550.1%
 
47.5225440.1%
 
47.618940.1%
 
47.6007140.1%
 
47.623940.1%
 
47.6201440.1%
 
47.582940.1%
 
47.5993840.1%
 
47.6104840.1%
 
47.676340.1%
 
47.6042740.1%
 
47.613940.1%
 
47.6616430.1%
 
47.617230.1%
 
47.6080530.1%
 
47.7083330.1%
 
47.6131930.1%
 
47.6247330.1%
 
47.6285530.1%
 
47.6217730.1%
 
Other values (2851)327196.9%
 
ValueCountFrequency (%) 
47.499171< 0.1%
 
47.500618951< 0.1%
 
47.502241< 0.1%
 
47.509591< 0.1%
 
47.50971< 0.1%
 
47.510181< 0.1%
 
47.510421< 0.1%
 
47.510981< 0.1%
 
47.511041< 0.1%
 
47.5112720.1%
 
ValueCountFrequency (%) 
47.733871< 0.1%
 
47.733751< 0.1%
 
47.733681< 0.1%
 
47.73361< 0.1%
 
47.733571< 0.1%
 
47.733511< 0.1%
 
47.733311< 0.1%
 
47.733161< 0.1%
 
47.733151< 0.1%
 
47.732791< 0.1%
 

Longitude
Real number (ℝ)

Distinct2656
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.3347952
Minimum-122.41425
Maximum-122.2209659
Zeros0
Zeros (%)0.0%
Memory size26.5 KiB
2020-12-12T14:45:19.676383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-122.41425
5-th percentile-122.3865425
Q1-122.3506625
median-122.332495
Q3-122.3194075
95-th percentile-122.2898275
Maximum-122.2209659
Range0.1932841
Interquartile range (IQR)0.031255

Descriptive statistics

Standard deviation0.02720328528
Coefficient of variation (CV)-0.0002223675221
Kurtosis0.2623984964
Mean-122.3347952
Median Absolute Deviation (MAD)0.01513
Skewness-0.1375264709
Sum-413002.2686
Variance0.00074001873
MonotocityNot monotonic
2020-12-12T14:45:19.756952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-122.2989880.2%
 
-122.3539870.2%
 
-122.3337960.2%
 
-122.3336960.2%
 
-122.3246860.2%
 
-122.3306450.1%
 
-122.3259250.1%
 
-122.3241750.1%
 
-122.3176950.1%
 
-122.3254440.1%
 
-122.3223240.1%
 
-122.3270540.1%
 
-122.3271440.1%
 
-122.3348640.1%
 
-122.3331640.1%
 
-122.3253240.1%
 
-122.3271740.1%
 
-122.325540.1%
 
-122.3326340.1%
 
-122.333940.1%
 
-122.3258540.1%
 
-122.3662740.1%
 
-122.32140.1%
 
-122.3228340.1%
 
-122.3332840.1%
 
Other values (2631)325996.5%
 
ValueCountFrequency (%) 
-122.414251< 0.1%
 
-122.411821< 0.1%
 
-122.411781< 0.1%
 
-122.411691< 0.1%
 
-122.410371< 0.1%
 
-122.410361< 0.1%
 
-122.410311< 0.1%
 
-122.409761< 0.1%
 
-122.409741< 0.1%
 
-122.409011< 0.1%
 
ValueCountFrequency (%) 
-122.22096591< 0.1%
 
-122.258641< 0.1%
 
-122.260281< 0.1%
 
-122.260341< 0.1%
 
-122.2616620.1%
 
-122.261721< 0.1%
 
-122.261771< 0.1%
 
-122.26181< 0.1%
 
-122.262161< 0.1%
 
-122.262231< 0.1%
 

YearBuilt
Real number (ℝ≥0)

Distinct113
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968.573164
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Memory size26.5 KiB
2020-12-12T14:45:19.839523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1908
Q11948
median1975
Q31997
95-th percentile2012
Maximum2015
Range115
Interquartile range (IQR)49

Descriptive statistics

Standard deviation33.08815578
Coefficient of variation (CV)0.01680819204
Kurtosis-0.8713417711
Mean1968.573164
Median Absolute Deviation (MAD)24
Skewness-0.5394445573
Sum6645903
Variance1094.826053
MonotocityNot monotonic
2020-12-12T14:45:19.922595image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2000722.1%
 
2014672.0%
 
1989672.0%
 
2008662.0%
 
1988641.9%
 
1999641.9%
 
1968631.9%
 
1990601.8%
 
2001601.8%
 
2002591.7%
 
1910581.7%
 
1970571.7%
 
1969571.7%
 
1900551.6%
 
1925541.6%
 
1928521.5%
 
1979521.5%
 
2013511.5%
 
1960501.5%
 
1980481.4%
 
1985471.4%
 
1926451.3%
 
2006451.3%
 
1962451.3%
 
1967451.3%
 
Other values (88)197358.4%
 
ValueCountFrequency (%) 
1900551.6%
 
190180.2%
 
1902110.3%
 
190340.1%
 
1904150.4%
 
190590.3%
 
1906190.6%
 
1907310.9%
 
1908270.8%
 
1909320.9%
 
ValueCountFrequency (%) 
2015371.1%
 
2014672.0%
 
2013511.5%
 
2012351.0%
 
2011150.4%
 
2010240.7%
 
2009411.2%
 
2008662.0%
 
2007421.2%
 
2006451.3%
 

NumberofBuildings
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct17
Distinct (%)0.5%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.106888361
Minimum0
Maximum111
Zeros92
Zeros (%)2.7%
Memory size26.5 KiB
2020-12-12T14:45:19.995157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum111
Range111
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.108401751
Coefficient of variation (CV)1.904800723
Kurtosis2205.296217
Mean1.106888361
Median Absolute Deviation (MAD)0
Skewness43.39499472
Sum3728
Variance4.445357942
MonotocityNot monotonic
2020-12-12T14:45:20.052206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
1317594.0%
 
0922.7%
 
2371.1%
 
3220.7%
 
4120.4%
 
5100.3%
 
650.1%
 
830.1%
 
1420.1%
 
1020.1%
 
920.1%
 
111< 0.1%
 
271< 0.1%
 
71< 0.1%
 
161< 0.1%
 
231< 0.1%
 
1111< 0.1%
 
(Missing)80.2%
 
ValueCountFrequency (%) 
0922.7%
 
1317594.0%
 
2371.1%
 
3220.7%
 
4120.4%
 
5100.3%
 
650.1%
 
71< 0.1%
 
830.1%
 
920.1%
 
ValueCountFrequency (%) 
1111< 0.1%
 
271< 0.1%
 
231< 0.1%
 
161< 0.1%
 
1420.1%
 
111< 0.1%
 
1020.1%
 
920.1%
 
830.1%
 
71< 0.1%
 

NumberofFloors
Real number (ℝ≥0)

Distinct50
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.709123223
Minimum0
Maximum99
Zeros16
Zeros (%)0.5%
Memory size26.5 KiB
2020-12-12T14:45:20.124268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile12
Maximum99
Range99
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.494464797
Coefficient of variation (CV)1.166770232
Kurtosis55.95064463
Mean4.709123223
Median Absolute Deviation (MAD)2
Skewness5.922339745
Sum15898
Variance30.18914341
MonotocityNot monotonic
2020-12-12T14:45:20.203336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
469220.5%
 
369220.5%
 
146613.8%
 
243913.0%
 
63069.1%
 
52958.7%
 
71484.4%
 
8641.9%
 
11320.9%
 
10320.9%
 
13210.6%
 
12210.6%
 
9180.5%
 
0160.5%
 
14130.4%
 
1790.3%
 
1570.2%
 
2470.2%
 
2370.2%
 
1870.2%
 
1670.2%
 
2660.2%
 
4260.2%
 
3360.2%
 
1960.2%
 
Other values (25)531.6%
 
ValueCountFrequency (%) 
0160.5%
 
146613.8%
 
243913.0%
 
369220.5%
 
469220.5%
 
52958.7%
 
63069.1%
 
71484.4%
 
8641.9%
 
9180.5%
 
ValueCountFrequency (%) 
991< 0.1%
 
761< 0.1%
 
631< 0.1%
 
561< 0.1%
 
551< 0.1%
 
491< 0.1%
 
471< 0.1%
 
461< 0.1%
 
4260.2%
 
4130.1%
 

PropertyGFATotal
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct3195
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94833.53732
Minimum11285
Maximum9320156
Zeros0
Zeros (%)0.0%
Memory size26.5 KiB
2020-12-12T14:45:20.281904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum11285
5-th percentile21291.5
Q128487
median44175
Q390992
95-th percentile320096
Maximum9320156
Range9308871
Interquartile range (IQR)62505

Descriptive statistics

Standard deviation218837.6071
Coefficient of variation (CV)2.30759722
Kurtosis946.2394908
Mean94833.53732
Median Absolute Deviation (MAD)19739.5
Skewness24.12940742
Sum320158022
Variance4.788989829e+10
MonotocityNot monotonic
2020-12-12T14:45:20.360471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3600090.3%
 
2592080.2%
 
2160070.2%
 
2880070.2%
 
2400060.2%
 
3024040.1%
 
3072040.1%
 
2232040.1%
 
2234430.1%
 
4500030.1%
 
2538030.1%
 
2580030.1%
 
2310030.1%
 
2000030.1%
 
2120030.1%
 
3330030.1%
 
3000030.1%
 
9000030.1%
 
2520030.1%
 
2190030.1%
 
2428830.1%
 
3190030.1%
 
4338030.1%
 
2323620.1%
 
2320020.1%
 
Other values (3170)327897.1%
 
ValueCountFrequency (%) 
112851< 0.1%
 
116851< 0.1%
 
119681< 0.1%
 
122941< 0.1%
 
127691< 0.1%
 
131571< 0.1%
 
136611< 0.1%
 
141011< 0.1%
 
153981< 0.1%
 
160001< 0.1%
 
ValueCountFrequency (%) 
93201561< 0.1%
 
22000001< 0.1%
 
19522201< 0.1%
 
17659701< 0.1%
 
16055781< 0.1%
 
15929141< 0.1%
 
15859601< 0.1%
 
15366061< 0.1%
 
140000020.1%
 
13809591< 0.1%
 

PropertyGFAParking
Real number (ℝ≥0)

ZEROS

Distinct496
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8001.526066
Minimum0
Maximum512608
Zeros2872
Zeros (%)85.1%
Memory size26.5 KiB
2020-12-12T14:45:20.437038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile46400.75
Maximum512608
Range512608
Interquartile range (IQR)0

Descriptive statistics

Standard deviation32326.72393
Coefficient of variation (CV)4.040069814
Kurtosis58.97489179
Mean8001.526066
Median Absolute Deviation (MAD)0
Skewness6.651190825
Sum27013152
Variance1045017080
MonotocityNot monotonic
2020-12-12T14:45:20.517607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0287285.1%
 
1332030.1%
 
2200020.1%
 
10017620.1%
 
1080020.1%
 
1296020.1%
 
3000020.1%
 
2580020.1%
 
2041620.1%
 
481791< 0.1%
 
137001< 0.1%
 
1242161< 0.1%
 
259201< 0.1%
 
259301< 0.1%
 
1099051< 0.1%
 
136601< 0.1%
 
2018571< 0.1%
 
54801< 0.1%
 
13921< 0.1%
 
751341< 0.1%
 
34591< 0.1%
 
92261< 0.1%
 
157551< 0.1%
 
156591< 0.1%
 
75651< 0.1%
 
Other values (471)47114.0%
 
ValueCountFrequency (%) 
0287285.1%
 
381< 0.1%
 
2601< 0.1%
 
4151< 0.1%
 
6041< 0.1%
 
7561< 0.1%
 
8001< 0.1%
 
9191< 0.1%
 
12631< 0.1%
 
13921< 0.1%
 
ValueCountFrequency (%) 
5126081< 0.1%
 
4077951< 0.1%
 
3898601< 0.1%
 
3689801< 0.1%
 
3351091< 0.1%
 
3276801< 0.1%
 
3194001< 0.1%
 
3037071< 0.1%
 
2856881< 0.1%
 
2850001< 0.1%
 

PropertyGFABuilding(s)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct3193
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86832.01126
Minimum3636
Maximum9320156
Zeros0
Zeros (%)0.0%
Memory size26.5 KiB
2020-12-12T14:45:20.597175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3636
5-th percentile21021
Q127756
median43216
Q384276.25
95-th percentile282658.5
Maximum9320156
Range9316520
Interquartile range (IQR)56520.25

Descriptive statistics

Standard deviation207939.8119
Coefficient of variation (CV)2.39473679
Kurtosis1161.360271
Mean86832.01126
Median Absolute Deviation (MAD)18958.5
Skewness27.62439064
Sum293144870
Variance4.323896538e+10
MonotocityNot monotonic
2020-12-12T14:45:20.674742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3600090.3%
 
2592080.2%
 
2160070.2%
 
2880070.2%
 
2400060.2%
 
2232040.1%
 
3024040.1%
 
3072040.1%
 
4500030.1%
 
2520030.1%
 
2538030.1%
 
2190030.1%
 
3000030.1%
 
2428830.1%
 
2310030.1%
 
3190030.1%
 
2234430.1%
 
4338030.1%
 
2000030.1%
 
3330030.1%
 
2120030.1%
 
2580030.1%
 
2808020.1%
 
2748020.1%
 
2323620.1%
 
Other values (3168)327997.1%
 
ValueCountFrequency (%) 
36361< 0.1%
 
109251< 0.1%
 
112851< 0.1%
 
114401< 0.1%
 
116851< 0.1%
 
119681< 0.1%
 
122941< 0.1%
 
127691< 0.1%
 
128061< 0.1%
 
131571< 0.1%
 
ValueCountFrequency (%) 
93201561< 0.1%
 
22000001< 0.1%
 
17659701< 0.1%
 
16328201< 0.1%
 
15929141< 0.1%
 
14000001< 0.1%
 
13809591< 0.1%
 
13230551< 0.1%
 
12582801< 0.1%
 
12157181< 0.1%
 

ListOfAllPropertyUseTypes
Categorical

HIGH CARDINALITY

Distinct466
Distinct (%)13.8%
Missing9
Missing (%)0.3%
Memory size26.5 KiB
Multifamily Housing
866 
Multifamily Housing, Parking
464 
Office
 
139
K-12 School
 
135
Office, Parking
 
120
Other values (461)
1643 
ValueCountFrequency (%) 
Multifamily Housing86625.7%
 
Multifamily Housing, Parking46413.7%
 
Office1394.1%
 
K-12 School1354.0%
 
Office, Parking1203.6%
 
Non-Refrigerated Warehouse1013.0%
 
Other611.8%
 
Non-Refrigerated Warehouse, Office611.8%
 
Retail Store561.7%
 
Multifamily Housing, Retail Store521.5%
 
Multifamily Housing, Parking, Retail Store511.5%
 
Worship Facility481.4%
 
Hotel441.3%
 
Office, Retail Store431.3%
 
Multifamily Housing, Office, Parking401.2%
 
Multifamily Housing, Office260.8%
 
Distribution Center240.7%
 
Self-Storage Facility230.7%
 
College/University210.6%
 
Medical Office200.6%
 
Supermarket/Grocery Store200.6%
 
Senior Care Community200.6%
 
Office, Other200.6%
 
Office, Other, Parking180.5%
 
Parking, Senior Care Community180.5%
 
Other values (441)87625.9%
 
2020-12-12T14:45:20.757813image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique314 ?
Unique (%)9.3%
2020-12-12T14:45:20.839384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length255
Median length20
Mean length25.87322275
Min length3

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i936410.7%
 
65377.5%
 
e56836.5%
 
a52616.0%
 
l49575.7%
 
t49465.7%
 
u42614.9%
 
r42474.9%
 
n41954.8%
 
o40144.6%
 
f39674.5%
 
g33103.8%
 
s27033.1%
 
,26833.1%
 
y21712.5%
 
m21352.4%
 
H19092.2%
 
M18562.1%
 
c18102.1%
 
O13991.6%
 
P12381.4%
 
k11821.4%
 
h10861.2%
 
S10331.2%
 
R9631.1%
 
Other values (27)44385.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter6638876.0%
 
Uppercase Letter1035911.9%
 
Space Separator65377.5%
 
Other Punctuation30593.5%
 
Dash Punctuation6330.7%
 
Decimal Number2940.3%
 
Open Punctuation39< 0.1%
 
Close Punctuation39< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H190918.4%
 
M185617.9%
 
O139913.5%
 
P123812.0%
 
S103310.0%
 
R9639.3%
 
W3563.4%
 
C3433.3%
 
N2682.6%
 
F2242.2%
 
D1771.7%
 
K1471.4%
 
G1121.1%
 
B860.8%
 
L600.6%
 
E550.5%
 
A550.5%
 
U380.4%
 
I150.1%
 
V130.1%
 
T120.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i936414.1%
 
e56838.6%
 
a52617.9%
 
l49577.5%
 
t49467.5%
 
u42616.4%
 
r42476.4%
 
n41956.3%
 
o40146.0%
 
f39676.0%
 
g33105.0%
 
s27034.1%
 
y21713.3%
 
m21353.2%
 
c18102.7%
 
k11821.8%
 
h10861.6%
 
d4980.8%
 
b2250.3%
 
p2030.3%
 
v1160.2%
 
w540.1%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
,268387.7%
 
/36411.9%
 
&120.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
6537100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-633100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
114750.0%
 
214750.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(39100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)39100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin7674787.9%
 
Common1060112.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i936412.2%
 
e56837.4%
 
a52616.9%
 
l49576.5%
 
t49466.4%
 
u42615.6%
 
r42475.5%
 
n41955.5%
 
o40145.2%
 
f39675.2%
 
g33104.3%
 
s27033.5%
 
y21712.8%
 
m21352.8%
 
H19092.5%
 
M18562.4%
 
c18102.4%
 
O13991.8%
 
P12381.6%
 
k11821.5%
 
h10861.4%
 
S10331.3%
 
R9631.3%
 
d4980.6%
 
W3560.5%
 
Other values (18)22032.9%
 

Most frequent Common characters

ValueCountFrequency (%) 
653761.7%
 
,268325.3%
 
-6336.0%
 
/3643.4%
 
11471.4%
 
21471.4%
 
(390.4%
 
)390.4%
 
&120.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII87348100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i936410.7%
 
65377.5%
 
e56836.5%
 
a52616.0%
 
l49575.7%
 
t49465.7%
 
u42614.9%
 
r42474.9%
 
n41954.8%
 
o40144.6%
 
f39674.5%
 
g33103.8%
 
s27033.1%
 
,26833.1%
 
y21712.5%
 
m21352.4%
 
H19092.2%
 
M18562.1%
 
c18102.1%
 
O13991.6%
 
P12381.4%
 
k11821.4%
 
h10861.2%
 
S10331.2%
 
R9631.1%
 
Other values (27)44385.1%
 

LargestPropertyUseType
Categorical

HIGH CARDINALITY

Distinct56
Distinct (%)1.7%
Missing20
Missing (%)0.6%
Memory size26.5 KiB
Multifamily Housing
1667 
Office
498 
Non-Refrigerated Warehouse
199 
K-12 School
 
139
Other
 
102
Other values (51)
751 
ValueCountFrequency (%) 
Multifamily Housing166749.4%
 
Office49814.8%
 
Non-Refrigerated Warehouse1995.9%
 
K-12 School1394.1%
 
Other1023.0%
 
Retail Store992.9%
 
Hotel772.3%
 
Worship Facility712.1%
 
Distribution Center541.6%
 
Senior Care Community461.4%
 
Supermarket/Grocery Store411.2%
 
Medical Office411.2%
 
Parking320.9%
 
Other - Recreation310.9%
 
Self-Storage Facility280.8%
 
College/University250.7%
 
Residence Hall/Dormitory240.7%
 
Other - Entertainment/Public Assembly210.6%
 
Laboratory130.4%
 
Restaurant120.4%
 
Refrigerated Warehouse120.4%
 
Hospital (General Medical & Surgical)100.3%
 
Social/Meeting Hall100.3%
 
Manufacturing/Industrial Plant80.2%
 
Strip Mall60.2%
 
Other values (31)902.7%
 
(Missing)200.6%
 
2020-12-12T14:45:20.927960image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)0.3%
2020-12-12T14:45:21.008529image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length52
Median length19
Mean length16.17742891
Min length3

Overview of Unicode Properties

Unique unicode characters51
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i682512.5%
 
l41307.6%
 
u37946.9%
 
t30955.7%
 
o30715.6%
 
e30315.5%
 
f30055.5%
 
a28205.2%
 
27965.1%
 
n24314.5%
 
s21904.0%
 
g20113.7%
 
y19583.6%
 
m18903.5%
 
H17983.3%
 
M17523.2%
 
r17513.2%
 
c10481.9%
 
O7341.3%
 
h6461.2%
 
S4760.9%
 
-4450.8%
 
R3920.7%
 
d3160.6%
 
W2830.5%
 
Other values (26)19273.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter4441581.3%
 
Uppercase Letter645111.8%
 
Space Separator27965.1%
 
Dash Punctuation4450.8%
 
Decimal Number2780.5%
 
Other Punctuation1960.4%
 
Open Punctuation17< 0.1%
 
Close Punctuation17< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
H179827.9%
 
M175227.2%
 
O73411.4%
 
S4767.4%
 
R3926.1%
 
W2834.4%
 
C2003.1%
 
N1993.1%
 
K1392.2%
 
F1111.7%
 
D891.4%
 
P731.1%
 
G560.9%
 
A310.5%
 
L310.5%
 
U310.5%
 
E270.4%
 
I110.2%
 
B110.2%
 
V60.1%
 
T1< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i682515.4%
 
l41309.3%
 
u37948.5%
 
t30957.0%
 
o30716.9%
 
e30316.8%
 
f30056.8%
 
a28206.3%
 
n24315.5%
 
s21904.9%
 
g20114.5%
 
y19584.4%
 
m18904.3%
 
r17513.9%
 
c10482.4%
 
h6461.5%
 
d3160.7%
 
p1520.3%
 
b1260.3%
 
k830.2%
 
v420.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2796100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-445100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/16684.7%
 
,2010.2%
 
&105.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
113950.0%
 
213950.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(17100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)17100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin5086693.1%
 
Common37496.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i682513.4%
 
l41308.1%
 
u37947.5%
 
t30956.1%
 
o30716.0%
 
e30316.0%
 
f30055.9%
 
a28205.5%
 
n24314.8%
 
s21904.3%
 
g20114.0%
 
y19583.8%
 
m18903.7%
 
H17983.5%
 
M17523.4%
 
r17513.4%
 
c10482.1%
 
O7341.4%
 
h6461.3%
 
S4760.9%
 
R3920.8%
 
d3160.6%
 
W2830.6%
 
C2000.4%
 
N1990.4%
 
Other values (17)10202.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
279674.6%
 
-44511.9%
 
/1664.4%
 
11393.7%
 
21393.7%
 
,200.5%
 
(170.5%
 
)170.5%
 
&100.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII54615100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i682512.5%
 
l41307.6%
 
u37946.9%
 
t30955.7%
 
o30715.6%
 
e30315.5%
 
f30055.5%
 
a28205.2%
 
27965.1%
 
n24314.5%
 
s21904.0%
 
g20113.7%
 
y19583.6%
 
m18903.5%
 
H17983.3%
 
M17523.2%
 
r17513.2%
 
c10481.9%
 
O7341.3%
 
h6461.2%
 
S4760.9%
 
-4450.8%
 
R3920.7%
 
d3160.6%
 
W2830.5%
 
Other values (26)19273.5%
 

LargestPropertyUseTypeGFA
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct3122
Distinct (%)93.0%
Missing20
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean79177.63856
Minimum5656
Maximum9320156
Zeros0
Zeros (%)0.0%
Memory size26.5 KiB
2020-12-12T14:45:21.083096image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum5656
5-th percentile17609
Q125094.75
median39894
Q376200.25
95-th percentile243388.5
Maximum9320156
Range9314500
Interquartile range (IQR)51105.5

Descriptive statistics

Standard deviation201703.4075
Coefficient of variation (CV)2.547479455
Kurtosis1320.609838
Mean79177.63856
Median Absolute Deviation (MAD)17574
Skewness30.09595071
Sum265720155
Variance4.068426459e+10
MonotocityNot monotonic
2020-12-12T14:45:21.160660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2400090.3%
 
2200090.3%
 
3000080.2%
 
2160080.2%
 
2000070.2%
 
2500060.2%
 
2880050.1%
 
3600050.1%
 
2428850.1%
 
1500050.1%
 
4500050.1%
 
6500040.1%
 
2460040.1%
 
3500040.1%
 
6000040.1%
 
1200040.1%
 
5000040.1%
 
2100040.1%
 
2190030.1%
 
1800030.1%
 
1600030.1%
 
3300030.1%
 
4656030.1%
 
10300030.1%
 
2360030.1%
 
Other values (3097)323595.8%
 
(Missing)200.6%
 
ValueCountFrequency (%) 
56561< 0.1%
 
64551< 0.1%
 
66011< 0.1%
 
69001< 0.1%
 
72451< 0.1%
 
73871< 0.1%
 
75011< 0.1%
 
75831< 0.1%
 
77581< 0.1%
 
80611< 0.1%
 
ValueCountFrequency (%) 
93201561< 0.1%
 
17196431< 0.1%
 
16809371< 0.1%
 
16393341< 0.1%
 
15859601< 0.1%
 
13501821< 0.1%
 
13144751< 0.1%
 
11911151< 0.1%
 
11721271< 0.1%
 
10720001< 0.1%
 

SecondLargestPropertyUseType
Categorical

MISSING

Distinct50
Distinct (%)3.0%
Missing1697
Missing (%)50.3%
Memory size26.5 KiB
Parking
976 
Office
215 
Retail Store
155 
Other
 
59
Restaurant
 
40
Other values (45)
234 
ValueCountFrequency (%) 
Parking97628.9%
 
Office2156.4%
 
Retail Store1554.6%
 
Other591.7%
 
Restaurant401.2%
 
Non-Refrigerated Warehouse331.0%
 
Multifamily Housing180.5%
 
Fitness Center/Health Club/Gym170.5%
 
Supermarket/Grocery Store140.4%
 
Data Center130.4%
 
Other - Services120.4%
 
Medical Office110.3%
 
Swimming Pool100.3%
 
Laboratory80.2%
 
Other - Entertainment/Public Assembly70.2%
 
Distribution Center70.2%
 
Bank Branch70.2%
 
K-12 School60.2%
 
Other - Restaurant/Bar50.1%
 
Self-Storage Facility40.1%
 
Repair Services (Vehicle, Shoe, Locksmith, etc)40.1%
 
Food Service40.1%
 
Worship Facility40.1%
 
Other - Recreation40.1%
 
Food Sales30.1%
 
Other values (25)431.3%
 
(Missing)169750.3%
 
2020-12-12T14:45:21.244732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique11 ?
Unique (%)0.7%
2020-12-12T14:45:21.323801image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length52
Median length3
Mean length6.072274882
Min length3

Overview of Unicode Properties

Unique unicode characters52
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n461622.5%
 
a319115.6%
 
i16478.0%
 
r16007.8%
 
e11935.8%
 
g10595.2%
 
P10024.9%
 
k10014.9%
 
t7733.8%
 
f5172.5%
 
4642.3%
 
o4012.0%
 
c3491.7%
 
l3341.6%
 
O3231.6%
 
S2471.2%
 
R2461.2%
 
s2031.0%
 
u1910.9%
 
h1890.9%
 
m970.5%
 
y800.4%
 
-790.4%
 
/730.4%
 
d690.3%
 
Other values (27)5562.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1763286.0%
 
Uppercase Letter221010.8%
 
Space Separator4642.3%
 
Other Punctuation890.4%
 
Dash Punctuation790.4%
 
Decimal Number120.1%
 
Open Punctuation7< 0.1%
 
Close Punctuation7< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n461626.2%
 
a319118.1%
 
i16479.3%
 
r16009.1%
 
e11936.8%
 
g10596.0%
 
k10015.7%
 
t7734.4%
 
f5172.9%
 
o4012.3%
 
c3492.0%
 
l3341.9%
 
s2031.2%
 
u1911.1%
 
h1891.1%
 
m970.6%
 
y800.5%
 
d690.4%
 
b540.3%
 
v290.2%
 
p280.2%
 
w110.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
P100245.3%
 
O32314.6%
 
S24711.2%
 
R24611.1%
 
C622.8%
 
H442.0%
 
W391.8%
 
M391.8%
 
N361.6%
 
F341.5%
 
G341.5%
 
D251.1%
 
B231.0%
 
L140.6%
 
A130.6%
 
E120.5%
 
K60.3%
 
V50.2%
 
T30.1%
 
I20.1%
 
U1< 0.1%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-79100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1650.0%
 
2650.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
464100.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(7100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/7382.0%
 
,1415.7%
 
&22.2%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)7100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1984296.8%
 
Common6583.2%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n461623.3%
 
a319116.1%
 
i16478.3%
 
r16008.1%
 
e11936.0%
 
g10595.3%
 
P10025.0%
 
k10015.0%
 
t7733.9%
 
f5172.6%
 
o4012.0%
 
c3491.8%
 
l3341.7%
 
O3231.6%
 
S2471.2%
 
R2461.2%
 
s2031.0%
 
u1911.0%
 
h1891.0%
 
m970.5%
 
y800.4%
 
d690.3%
 
C620.3%
 
b540.3%
 
H440.2%
 
Other values (18)3541.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
46470.5%
 
-7912.0%
 
/7311.1%
 
,142.1%
 
(71.1%
 
)71.1%
 
160.9%
 
260.9%
 
&20.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII20500100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n461622.5%
 
a319115.6%
 
i16478.0%
 
r16007.8%
 
e11935.8%
 
g10595.2%
 
P10024.9%
 
k10014.9%
 
t7733.8%
 
f5172.5%
 
4642.3%
 
o4012.0%
 
c3491.7%
 
l3341.6%
 
O3231.6%
 
S2471.2%
 
R2461.2%
 
s2031.0%
 
u1910.9%
 
h1890.9%
 
m970.5%
 
y800.4%
 
-790.4%
 
/730.4%
 
d690.3%
 
Other values (27)5562.7%
 

SecondLargestPropertyUseTypeGFA
Real number (ℝ≥0)

MISSING
ZEROS

Distinct1352
Distinct (%)80.5%
Missing1697
Missing (%)50.3%
Infinite0
Infinite (%)0.0%
Mean28444.07582
Minimum0
Maximum686750
Zeros126
Zeros (%)3.7%
Memory size26.5 KiB
2020-12-12T14:45:21.397364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15000
median10664
Q326640
95-th percentile117338.6
Maximum686750
Range686750
Interquartile range (IQR)21640

Descriptive statistics

Standard deviation54392.91793
Coefficient of variation (CV)1.912275803
Kurtosis36.30208308
Mean28444.07582
Median Absolute Deviation (MAD)7564
Skewness5.033480723
Sum47757603.3
Variance2958589521
MonotocityNot monotonic
2020-12-12T14:45:21.473429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01263.7%
 
5000140.4%
 
15000120.4%
 
7200120.4%
 
6000120.4%
 
700090.3%
 
1000080.2%
 
400070.2%
 
800070.2%
 
150060.2%
 
100050.1%
 
300050.1%
 
2000050.1%
 
450050.1%
 
1800050.1%
 
1200050.1%
 
1440040.1%
 
900040.1%
 
3000040.1%
 
750040.1%
 
2400040.1%
 
350040.1%
 
630040.1%
 
250040.1%
 
787530.1%
 
Other values (1327)140141.5%
 
(Missing)169750.3%
 
ValueCountFrequency (%) 
01263.7%
 
21< 0.1%
 
401< 0.1%
 
2001< 0.1%
 
2201< 0.1%
 
3001< 0.1%
 
3201< 0.1%
 
3631< 0.1%
 
40020.1%
 
4061< 0.1%
 
ValueCountFrequency (%) 
6867501< 0.1%
 
6399311< 0.1%
 
4415511< 0.1%
 
4387561< 0.1%
 
3898601< 0.1%
 
3876511< 0.1%
 
3806391< 0.1%
 
3770461< 0.1%
 
3487881< 0.1%
 
3402361< 0.1%
 

ThirdLargestPropertyUseType
Categorical

MISSING

Distinct44
Distinct (%)7.4%
Missing2780
Missing (%)82.3%
Memory size26.5 KiB
Retail Store
110 
Office
105 
Parking
71 
Restaurant
56 
Other
49 
Other values (39)
205 
ValueCountFrequency (%) 
Retail Store1103.3%
 
Office1053.1%
 
Parking712.1%
 
Restaurant561.7%
 
Other491.5%
 
Swimming Pool290.9%
 
Non-Refrigerated Warehouse180.5%
 
Medical Office170.5%
 
Data Center140.4%
 
Multifamily Housing120.4%
 
Food Service110.3%
 
Social/Meeting Hall110.3%
 
Other - Restaurant/Bar90.3%
 
Pre-school/Daycare80.2%
 
Personal Services (Health/Beauty, Dry Cleaning, etc)60.2%
 
Fitness Center/Health Club/Gym60.2%
 
Other - Entertainment/Public Assembly60.2%
 
Bank Branch60.2%
 
Financial Office50.1%
 
Distribution Center30.1%
 
Supermarket/Grocery Store30.1%
 
Bar/Nightclub30.1%
 
Other - Lodging/Residential30.1%
 
Food Sales30.1%
 
Fast Food Restaurant30.1%
 
Other values (19)290.9%
 
(Missing)278082.3%
 
2020-12-12T14:45:21.560504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique9 ?
Unique (%)1.5%
2020-12-12T14:45:21.635569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length52
Median length3
Mean length4.588270142
Min length3

Overview of Unicode Properties

Unique unicode characters51
Unique unicode categories8 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n588838.0%
 
a334221.6%
 
e8455.5%
 
t6174.0%
 
i5693.7%
 
r5183.3%
 
3742.4%
 
o3332.1%
 
l3032.0%
 
f2891.9%
 
c2431.6%
 
O2051.3%
 
R2041.3%
 
S1951.3%
 
s1661.1%
 
g1641.1%
 
u1470.9%
 
h1350.9%
 
P1220.8%
 
m920.6%
 
k800.5%
 
/670.4%
 
d640.4%
 
-580.4%
 
y580.4%
 
Other values (26)4122.7%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1394290.0%
 
Uppercase Letter10216.6%
 
Space Separator3742.4%
 
Other Punctuation790.5%
 
Dash Punctuation580.4%
 
Open Punctuation6< 0.1%
 
Close Punctuation6< 0.1%
 
Decimal Number4< 0.1%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n588842.2%
 
a334224.0%
 
e8456.1%
 
t6174.4%
 
i5694.1%
 
r5183.7%
 
o3332.4%
 
l3032.2%
 
f2892.1%
 
c2431.7%
 
s1661.2%
 
g1641.2%
 
u1471.1%
 
h1351.0%
 
m920.7%
 
k800.6%
 
d640.5%
 
y580.4%
 
w310.2%
 
b270.2%
 
v230.2%
 
p80.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
O20520.1%
 
R20420.0%
 
S19519.1%
 
P12211.9%
 
M434.2%
 
C383.7%
 
H383.7%
 
F343.3%
 
D323.1%
 
B302.9%
 
N212.1%
 
W212.1%
 
G111.1%
 
E80.8%
 
A60.6%
 
L50.5%
 
U20.2%
 
K20.2%
 
T20.2%
 
I10.1%
 
V10.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
374100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-58100.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/6784.8%
 
,1215.2%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
1250.0%
 
2250.0%
 

Most frequent Open Punctuation characters

ValueCountFrequency (%) 
(6100.0%
 

Most frequent Close Punctuation characters

ValueCountFrequency (%) 
)6100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1496396.6%
 
Common5273.4%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n588839.4%
 
a334222.3%
 
e8455.6%
 
t6174.1%
 
i5693.8%
 
r5183.5%
 
o3332.2%
 
l3032.0%
 
f2891.9%
 
c2431.6%
 
O2051.4%
 
R2041.4%
 
S1951.3%
 
s1661.1%
 
g1641.1%
 
u1471.0%
 
h1350.9%
 
P1220.8%
 
m920.6%
 
k800.5%
 
d640.4%
 
y580.4%
 
M430.3%
 
C380.3%
 
H380.3%
 
Other values (18)2651.8%
 

Most frequent Common characters

ValueCountFrequency (%) 
37471.0%
 
/6712.7%
 
-5811.0%
 
,122.3%
 
(61.1%
 
)61.1%
 
120.4%
 
220.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII15490100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n588838.0%
 
a334221.6%
 
e8455.5%
 
t6174.0%
 
i5693.7%
 
r5183.3%
 
3742.4%
 
o3332.1%
 
l3032.0%
 
f2891.9%
 
c2431.6%
 
O2051.3%
 
R2041.3%
 
S1951.3%
 
s1661.1%
 
g1641.1%
 
u1470.9%
 
h1350.9%
 
P1220.8%
 
m920.6%
 
k800.5%
 
/670.4%
 
d640.4%
 
-580.4%
 
y580.4%
 
Other values (26)4122.7%
 

ThirdLargestPropertyUseTypeGFA
Real number (ℝ≥0)

MISSING
ZEROS

Distinct501
Distinct (%)84.1%
Missing2780
Missing (%)82.3%
Infinite0
Infinite (%)0.0%
Mean11738.67517
Minimum0
Maximum459748
Zeros48
Zeros (%)1.4%
Memory size26.5 KiB
2020-12-12T14:45:21.705129image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12239
median5043
Q310138.75
95-th percentile41654.5
Maximum459748
Range459748
Interquartile range (IQR)7899.75

Descriptive statistics

Standard deviation29331.19929
Coefficient of variation (CV)2.498680547
Kurtosis114.1871141
Mean11738.67517
Median Absolute Deviation (MAD)3590.5
Skewness9.196935797
Sum6996250.399
Variance860319251.6
MonotocityNot monotonic
2020-12-12T14:45:21.783696image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0481.4%
 
600070.2%
 
500060.2%
 
200050.1%
 
300050.1%
 
900040.1%
 
100040.1%
 
125030.1%
 
400030.1%
 
620030.1%
 
150030.1%
 
250030.1%
 
544420.1%
 
450020.1%
 
720020.1%
 
550020.1%
 
340020.1%
 
270020.1%
 
457920.1%
 
700020.1%
 
1000020.1%
 
235120.1%
 
750020.1%
 
968020.1%
 
370020.1%
 
Other values (476)47614.1%
 
(Missing)278082.3%
 
ValueCountFrequency (%) 
0481.4%
 
1821< 0.1%
 
1871< 0.1%
 
2401< 0.1%
 
2501< 0.1%
 
2851< 0.1%
 
2941< 0.1%
 
4001< 0.1%
 
4041< 0.1%
 
4361< 0.1%
 
ValueCountFrequency (%) 
4597481< 0.1%
 
3039101< 0.1%
 
2203031< 0.1%
 
1772101< 0.1%
 
1414501< 0.1%
 
1335981< 0.1%
 
1034781< 0.1%
 
1032001< 0.1%
 
889011< 0.1%
 
84051.898441< 0.1%
 

YearsENERGYSTARCertified
Categorical

HIGH CARDINALITY
MISSING

Distinct65
Distinct (%)54.6%
Missing3257
Missing (%)96.5%
Memory size26.5 KiB
2016
14 
20172016
8 
2017
 
7
20162015
 
6
2014
 
6
Other values (60)
78 
ValueCountFrequency (%) 
2016140.4%
 
2017201680.2%
 
201770.2%
 
2016201560.2%
 
201460.2%
 
200940.1%
 
201340.1%
 
2015201430.1%
 
20162015201430.1%
 
2017201530.1%
 
20172016201520.1%
 
2011200920.1%
 
2009200520.1%
 
201520.1%
 
20162009200820.1%
 
201020.1%
 
2016201420122011200820071< 0.1%
 
201620121< 0.1%
 
201620152013201220101< 0.1%
 
201620112010200920081< 0.1%
 
2017201020071< 0.1%
 
2015201420131< 0.1%
 
20172015201420131< 0.1%
 
2016201520142013201120091< 0.1%
 
2017201620142013201220112010200920071< 0.1%
 
Other values (40)401.2%
 
(Missing)325796.5%
 
2020-12-12T14:45:21.871272image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique49 ?
Unique (%)41.2%
2020-12-12T14:45:21.946837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length60
Median length3
Mean length3.325533175
Min length3

Overview of Unicode Properties

Unique unicode characters12
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n651458.0%
 
a325729.0%
 
04744.2%
 
23913.5%
 
13032.7%
 
6670.6%
 
5510.5%
 
7460.4%
 
4380.3%
 
9330.3%
 
3290.3%
 
8240.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter977187.0%
 
Decimal Number145613.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n651466.7%
 
a325733.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
047432.6%
 
239126.9%
 
130320.8%
 
6674.6%
 
5513.5%
 
7463.2%
 
4382.6%
 
9332.3%
 
3292.0%
 
8241.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin977187.0%
 
Common145613.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n651466.7%
 
a325733.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
047432.6%
 
239126.9%
 
130320.8%
 
6674.6%
 
5513.5%
 
7463.2%
 
4382.6%
 
9332.3%
 
3292.0%
 
8241.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII11227100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n651458.0%
 
a325729.0%
 
04744.2%
 
23913.5%
 
13032.7%
 
6670.6%
 
5510.5%
 
7460.4%
 
4380.3%
 
9330.3%
 
3290.3%
 
8240.2%
 

ENERGYSTARScore
Real number (ℝ≥0)

MISSING

Distinct100
Distinct (%)3.9%
Missing843
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean67.91867351
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Memory size26.5 KiB
2020-12-12T14:45:22.017898image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q153
median75
Q390
95-th percentile99
Maximum100
Range99
Interquartile range (IQR)37

Descriptive statistics

Standard deviation26.87327089
Coefficient of variation (CV)0.3956683707
Kurtosis-0.2195668767
Mean67.91867351
Median Absolute Deviation (MAD)17
Skewness-0.8594613198
Sum172038
Variance722.1726883
MonotocityNot monotonic
2020-12-12T14:45:22.098467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1001093.2%
 
98722.1%
 
96641.9%
 
89581.7%
 
93571.7%
 
92531.6%
 
95511.5%
 
99491.5%
 
94491.5%
 
91491.5%
 
97481.4%
 
83481.4%
 
90471.4%
 
81461.4%
 
84461.4%
 
79461.4%
 
86451.3%
 
88451.3%
 
87441.3%
 
76431.3%
 
72421.2%
 
80411.2%
 
75411.2%
 
69411.2%
 
82401.2%
 
Other values (75)125937.3%
 
(Missing)84325.0%
 
ValueCountFrequency (%) 
1361.1%
 
2100.3%
 
3130.4%
 
450.1%
 
5100.3%
 
680.2%
 
7100.3%
 
8100.3%
 
950.1%
 
10100.3%
 
ValueCountFrequency (%) 
1001093.2%
 
99491.5%
 
98722.1%
 
97481.4%
 
96641.9%
 
95511.5%
 
94491.5%
 
93571.7%
 
92531.6%
 
91491.5%
 

SiteEUI(kBtu/sf)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1085
Distinct (%)32.2%
Missing7
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean54.7321164
Minimum0
Maximum834.4000244
Zeros16
Zeros (%)0.5%
Memory size26.5 KiB
2020-12-12T14:45:22.180037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16.97999992
Q127.89999962
median38.59999847
Q360.40000153
95-th percentile146.9
Maximum834.4000244
Range834.4000244
Interquartile range (IQR)32.50000191

Descriptive statistics

Standard deviation56.27312409
Coefficient of variation (CV)1.028155456
Kurtosis39.99456818
Mean54.7321164
Median Absolute Deviation (MAD)13.5
Skewness4.981885737
Sum184392.5001
Variance3166.664495
MonotocityNot monotonic
2020-12-12T14:45:22.255602image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
28.79999924170.5%
 
24.70000076170.5%
 
0160.5%
 
24.20000076160.5%
 
32150.4%
 
26.39999962140.4%
 
31.70000076140.4%
 
28.89999962140.4%
 
29.60000038130.4%
 
26.60000038130.4%
 
30.60000038130.4%
 
22.79999924130.4%
 
31.5120.4%
 
29120.4%
 
30.89999962120.4%
 
26.70000076120.4%
 
27.89999962120.4%
 
27.10000038120.4%
 
31.10000038120.4%
 
29.10000038110.3%
 
28.5110.3%
 
32.70000076110.3%
 
28.10000038110.3%
 
26.10000038110.3%
 
26.79999924110.3%
 
Other values (1060)304490.2%
 
ValueCountFrequency (%) 
0160.5%
 
0.4000000061< 0.1%
 
0.6999999881< 0.1%
 
11< 0.1%
 
1.3999999761< 0.1%
 
1.79999995220.1%
 
2.0999999051< 0.1%
 
2.2999999521< 0.1%
 
31< 0.1%
 
3.2000000481< 0.1%
 
ValueCountFrequency (%) 
834.40002441< 0.1%
 
707.29998781< 0.1%
 
696.70001221< 0.1%
 
694.70001221< 0.1%
 
639.70001221< 0.1%
 
593.59997561< 0.1%
 
465.51< 0.1%
 
456.60000611< 0.1%
 
438.20001221< 0.1%
 
412.70001221< 0.1%
 

SiteEUIWN(kBtu/sf)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1105
Distinct (%)32.8%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean57.03379823
Minimum0
Maximum834.4000244
Zeros29
Zeros (%)0.9%
Memory size26.5 KiB
2020-12-12T14:45:22.333670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.39999962
Q129.39999962
median40.90000153
Q364.27500153
95-th percentile149.155001
Maximum834.4000244
Range834.4000244
Interquartile range (IQR)34.87500191

Descriptive statistics

Standard deviation57.16333021
Coefficient of variation (CV)1.002271144
Kurtosis37.63950264
Mean57.03379823
Median Absolute Deviation (MAD)14.30000115
Skewness4.827517733
Sum192203.9
Variance3267.646321
MonotocityNot monotonic
2020-12-12T14:45:22.407733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0290.9%
 
29.5170.5%
 
30.79999924150.4%
 
27.89999962140.4%
 
32.20000076140.4%
 
30.20000076140.4%
 
29140.4%
 
31.60000038140.4%
 
31.39999962130.4%
 
33.59999847130.4%
 
28.10000038130.4%
 
29.10000038120.4%
 
34.5120.4%
 
34.09999847120.4%
 
25.10000038120.4%
 
24.60000038120.4%
 
34.29999924120.4%
 
28120.4%
 
27.39999962110.3%
 
42.79999924110.3%
 
29.20000076110.3%
 
38110.3%
 
30.39999962110.3%
 
37.29999924110.3%
 
31.20000076110.3%
 
Other values (1080)303990.0%
 
ValueCountFrequency (%) 
0290.9%
 
0.4000000061< 0.1%
 
0.6999999881< 0.1%
 
11< 0.1%
 
1.51< 0.1%
 
1.79999995220.1%
 
2.0999999051< 0.1%
 
2.2999999521< 0.1%
 
31< 0.1%
 
3.2000000481< 0.1%
 
ValueCountFrequency (%) 
834.40002441< 0.1%
 
707.29998781< 0.1%
 
694.70001221< 0.1%
 
693.09997561< 0.1%
 
639.79998781< 0.1%
 
593.59997561< 0.1%
 
468.70001221< 0.1%
 
4671< 0.1%
 
460.10000611< 0.1%
 
426.60000611< 0.1%
 

SourceEUI(kBtu/sf)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1648
Distinct (%)48.9%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean134.2328482
Minimum0
Maximum2620
Zeros24
Zeros (%)0.7%
Memory size26.5 KiB
2020-12-12T14:45:22.482798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.85999947
Q174.69999695
median96.19999695
Q3143.8999939
95-th percentile351.6700104
Maximum2620
Range2620
Interquartile range (IQR)69.19999695

Descriptive statistics

Standard deviation139.2875538
Coefficient of variation (CV)1.037656249
Kurtosis77.66477838
Mean134.2328482
Median Absolute Deviation (MAD)27.79999542
Skewness6.595043734
Sum451962
Variance19401.02264
MonotocityNot monotonic
2020-12-12T14:45:22.564368image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0240.7%
 
83.6999969590.3%
 
68.0999984790.3%
 
69.6999969580.2%
 
87.6999969580.2%
 
90.580.2%
 
9580.2%
 
94.0999984780.2%
 
73.0999984780.2%
 
78.5999984780.2%
 
98.8000030570.2%
 
70.5999984770.2%
 
79.3000030570.2%
 
88.0999984770.2%
 
93.6999969570.2%
 
80.8000030570.2%
 
90.4000015370.2%
 
81.8000030570.2%
 
6670.2%
 
77.6999969570.2%
 
91.570.2%
 
63.2999992470.2%
 
5970.2%
 
8470.2%
 
68.9000015370.2%
 
Other values (1623)316493.7%
 
(Missing)90.3%
 
ValueCountFrequency (%) 
0240.7%
 
1.1000000241< 0.1%
 
21< 0.1%
 
2.0999999051< 0.1%
 
31< 0.1%
 
4.3000001911< 0.1%
 
4.51< 0.1%
 
5.80000019120.1%
 
6.4000000951< 0.1%
 
6.59999990520.1%
 
ValueCountFrequency (%) 
26201< 0.1%
 
2217.8000491< 0.1%
 
2181.3000491< 0.1%
 
2007.9000241< 0.1%
 
1527.3000491< 0.1%
 
1206.6999511< 0.1%
 
1150.3000491< 0.1%
 
1026.5999761< 0.1%
 
978.90002441< 0.1%
 
962.09997561< 0.1%
 

SourceEUIWN(kBtu/sf)
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct1694
Distinct (%)50.3%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean137.7839323
Minimum-2.099999905
Maximum2620
Zeros36
Zeros (%)1.1%
Memory size26.5 KiB
2020-12-12T14:45:22.643937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.099999905
5-th percentile37.70000076
Q178.40000153
median101.0999985
Q3148.3499985
95-th percentile353.8600037
Maximum2620
Range2622.1
Interquartile range (IQR)69.94999697

Descriptive statistics

Standard deviation139.1098067
Coefficient of variation (CV)1.009622852
Kurtosis77.44186155
Mean137.7839323
Median Absolute Deviation (MAD)28.50000003
Skewness6.569688358
Sum463918.5
Variance19351.53831
MonotocityNot monotonic
2020-12-12T14:45:22.725507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0361.1%
 
87.3000030590.3%
 
73.5999984790.3%
 
93.5999984780.2%
 
104.599998580.2%
 
83.580.2%
 
84.9000015380.2%
 
98.9000015380.2%
 
102.400001580.2%
 
75.580.2%
 
89.0999984770.2%
 
72.3000030570.2%
 
77.1999969570.2%
 
86.9000015370.2%
 
92.5999984770.2%
 
80.6999969570.2%
 
90.3000030570.2%
 
98.570.2%
 
104.099998570.2%
 
73.8000030570.2%
 
93.4000015370.2%
 
87.8000030570.2%
 
10770.2%
 
117.099998570.2%
 
103.199996970.2%
 
Other values (1669)315293.4%
 
(Missing)90.3%
 
ValueCountFrequency (%) 
-2.0999999051< 0.1%
 
0361.1%
 
1.1000000241< 0.1%
 
2.2000000481< 0.1%
 
31< 0.1%
 
4.5999999051< 0.1%
 
5.4000000951< 0.1%
 
5.80000019120.1%
 
6.4000000951< 0.1%
 
6.5999999051< 0.1%
 
ValueCountFrequency (%) 
26201< 0.1%
 
2217.8000491< 0.1%
 
2181.3000491< 0.1%
 
20081< 0.1%
 
1527.3000491< 0.1%
 
1195.0999761< 0.1%
 
1138.4000241< 0.1%
 
10011< 0.1%
 
978.90002441< 0.1%
 
9541< 0.1%
 

SiteEnergyUse(kBtu)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct3354
Distinct (%)99.5%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean5403667.295
Minimum0
Maximum873923712
Zeros18
Zeros (%)0.5%
Memory size26.5 KiB
2020-12-12T14:45:22.806576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile491819.9531
Q1925128.5938
median1803753.25
Q34222455.25
95-th percentile18161625
Maximum873923712
Range873923712
Interquartile range (IQR)3297326.656

Descriptive statistics

Standard deviation21610628.63
Coefficient of variation (CV)3.999252258
Kurtosis858.6184814
Mean5403667.295
Median Absolute Deviation (MAD)1074356.062
Skewness24.84197927
Sum1.821576245e+10
Variance4.670192697e+14
MonotocityNot monotonic
2020-12-12T14:45:22.882642image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0180.5%
 
645451.81251< 0.1%
 
1201587.1251< 0.1%
 
1026736961< 0.1%
 
27635971< 0.1%
 
100057871< 0.1%
 
850430.8751< 0.1%
 
1119640.251< 0.1%
 
1146485201< 0.1%
 
67722891< 0.1%
 
2960159.751< 0.1%
 
159658591< 0.1%
 
1807738.251< 0.1%
 
6182350.51< 0.1%
 
331059.51< 0.1%
 
1152368.251< 0.1%
 
1332591.51< 0.1%
 
1594733.6251< 0.1%
 
767323.31251< 0.1%
 
1298307.6251< 0.1%
 
1987934.6251< 0.1%
 
576174.1251< 0.1%
 
1938776.751< 0.1%
 
936616.51< 0.1%
 
4515180.51< 0.1%
 
Other values (3329)332998.6%
 
(Missing)50.1%
 
ValueCountFrequency (%) 
0180.5%
 
134091< 0.1%
 
16808.900391< 0.1%
 
24105.51< 0.1%
 
44293.51< 0.1%
 
57133.199221< 0.1%
 
72370.398441< 0.1%
 
79711.796881< 0.1%
 
90558.703131< 0.1%
 
97690.398441< 0.1%
 
ValueCountFrequency (%) 
8739237121< 0.1%
 
4483853121< 0.1%
 
2930907841< 0.1%
 
2916144321< 0.1%
 
2746822081< 0.1%
 
2538324641< 0.1%
 
1639459841< 0.1%
 
1434230241< 0.1%
 
1313738801< 0.1%
 
1146485201< 0.1%
 

SiteEnergyUseWN(kBtu)
Real number (ℝ≥0)

Distinct3341
Distinct (%)99.1%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean5276725.714
Minimum0
Maximum471613856
Zeros29
Zeros (%)0.9%
Memory size26.5 KiB
2020-12-12T14:45:22.960209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile503320.811
Q1970182.2344
median1904452
Q34381429.125
95-th percentile18203297.4
Maximum471613856
Range471613856
Interquartile range (IQR)3411246.891

Descriptive statistics

Standard deviation15938786.48
Coefficient of variation (CV)3.020582715
Kurtosis334.5050175
Mean5276725.714
Median Absolute Deviation (MAD)1130097.25
Skewness15.26906663
Sum1.778256566e+10
Variance2.540449146e+14
MonotocityNot monotonic
2020-12-12T14:45:23.038776image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0290.9%
 
2127889.2520.1%
 
22062521< 0.1%
 
1987934.6251< 0.1%
 
3123892.751< 0.1%
 
6826681< 0.1%
 
7034167.51< 0.1%
 
11687041< 0.1%
 
856758.81251< 0.1%
 
373518041< 0.1%
 
8301001< 0.1%
 
198920501< 0.1%
 
928400.68751< 0.1%
 
5469793.51< 0.1%
 
682632.31251< 0.1%
 
431453.18751< 0.1%
 
4740119.51< 0.1%
 
814797.6251< 0.1%
 
1856762.751< 0.1%
 
1856761.8751< 0.1%
 
1430774.751< 0.1%
 
2697683.251< 0.1%
 
32875021< 0.1%
 
36807111< 0.1%
 
1103066.251< 0.1%
 
Other values (3316)331698.2%
 
(Missing)60.2%
 
ValueCountFrequency (%) 
0290.9%
 
134091< 0.1%
 
172601< 0.1%
 
24105.51< 0.1%
 
44293.51< 0.1%
 
58114.199221< 0.1%
 
72370.398441< 0.1%
 
79967.898441< 0.1%
 
90558.703131< 0.1%
 
98862.898441< 0.1%
 
ValueCountFrequency (%) 
4716138561< 0.1%
 
2966717441< 0.1%
 
2959298881< 0.1%
 
2747259841< 0.1%
 
2577642081< 0.1%
 
1672071041< 0.1%
 
1472990561< 0.1%
 
1371061121< 0.1%
 
1232055601< 0.1%
 
1039852641< 0.1%
 

SteamUse(kBtu)
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct131
Distinct (%)3.9%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean274595.8982
Minimum0
Maximum134943456
Zeros3237
Zeros (%)95.9%
Memory size26.5 KiB
2020-12-12T14:45:23.115342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum134943456
Range134943456
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3912173.393
Coefficient of variation (CV)14.24702051
Kurtosis804.8642147
Mean274595.8982
Median Absolute Deviation (MAD)0
Skewness26.72088824
Sum924564389.3
Variance1.530510065e+13
MonotocityNot monotonic
2020-12-12T14:45:23.200916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0323795.9%
 
340615.59381< 0.1%
 
328247.09381< 0.1%
 
714523.68751< 0.1%
 
5650343.51< 0.1%
 
1920474.251< 0.1%
 
3578548.251< 0.1%
 
2513519.251< 0.1%
 
34308621< 0.1%
 
42094661< 0.1%
 
1379517.751< 0.1%
 
4337738.51< 0.1%
 
1158118.251< 0.1%
 
3512026.51< 0.1%
 
3593940.251< 0.1%
 
3480880.751< 0.1%
 
65688321< 0.1%
 
4336524.51< 0.1%
 
21212031< 0.1%
 
166488.40631< 0.1%
 
41201301< 0.1%
 
162845701< 0.1%
 
882630.68751< 0.1%
 
3071838.51< 0.1%
 
997348.18751< 0.1%
 
Other values (106)1063.1%
 
(Missing)90.3%
 
ValueCountFrequency (%) 
0323795.9%
 
21230.800781< 0.1%
 
1379001< 0.1%
 
151742.51< 0.1%
 
166488.40631< 0.1%
 
1757801< 0.1%
 
180731.79691< 0.1%
 
2046501< 0.1%
 
230989.29691< 0.1%
 
2662621< 0.1%
 
ValueCountFrequency (%) 
1349434561< 0.1%
 
1225750321< 0.1%
 
849852401< 0.1%
 
738854721< 0.1%
 
310301941< 0.1%
 
284388841< 0.1%
 
215665541< 0.1%
 
196547621< 0.1%
 
185478581< 0.1%
 
175484161< 0.1%
 

Electricity(kWh)
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct3352
Distinct (%)99.6%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1086638.967
Minimum-33826.80078
Maximum192577488
Zeros14
Zeros (%)0.4%
Memory size26.5 KiB
2020-12-12T14:45:23.285989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-33826.80078
5-th percentile72675.63985
Q1187422.9453
median345129.9063
Q3829317.8438
95-th percentile3944193.125
Maximum192577488
Range192611314.8
Interquartile range (IQR)641894.8984

Descriptive statistics

Standard deviation4352478.355
Coefficient of variation (CV)4.005450282
Kurtosis1157.498858
Mean1086638.967
Median Absolute Deviation (MAD)200696.3125
Skewness28.72846386
Sum3658713400
Variance1.894406783e+13
MonotocityNot monotonic
2020-12-12T14:45:23.368560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0140.4%
 
239011.593820.1%
 
317841.406320.1%
 
193193.51< 0.1%
 
114820.29691< 0.1%
 
70999081< 0.1%
 
394591.68751< 0.1%
 
140686671< 0.1%
 
158390.70311< 0.1%
 
148879.20311< 0.1%
 
138864.79691< 0.1%
 
4210281< 0.1%
 
447831.68751< 0.1%
 
85064.101561< 0.1%
 
1162211< 0.1%
 
148718.90631< 0.1%
 
241308.90631< 0.1%
 
211624.51< 0.1%
 
148875.70311< 0.1%
 
235807.09381< 0.1%
 
304462.81251< 0.1%
 
186654.40631< 0.1%
 
1855803.751< 0.1%
 
144774.20311< 0.1%
 
28947.900391< 0.1%
 
Other values (3327)332798.5%
 
(Missing)90.3%
 
ValueCountFrequency (%) 
-33826.800781< 0.1%
 
0140.4%
 
11< 0.1%
 
1798.9000241< 0.1%
 
3332.51< 0.1%
 
39301< 0.1%
 
4913.51< 0.1%
 
4926.3999021< 0.1%
 
7064.8999021< 0.1%
 
7727.2001951< 0.1%
 
ValueCountFrequency (%) 
1925774881< 0.1%
 
804608721< 0.1%
 
494383361< 0.1%
 
441020761< 0.1%
 
408425641< 0.1%
 
338925001< 0.1%
 
263952221< 0.1%
 
257479081< 0.1%
 
219570201< 0.1%
 
201163021< 0.1%
 

Electricity(kBtu)
Real number (ℝ)

HIGH CORRELATION
SKEWED

Distinct3351
Distinct (%)99.5%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean3707612.162
Minimum-115417
Maximum657074389
Zeros14
Zeros (%)0.4%
Memory size26.5 KiB
2020-12-12T14:45:23.454134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-115417
5-th percentile247969.2
Q1639487
median1177583
Q32829632.5
95-th percentile13457586.8
Maximum657074389
Range657189806
Interquartile range (IQR)2190145.5

Descriptive statistics

Standard deviation14850656.14
Coefficient of variation (CV)4.005450271
Kurtosis1157.498861
Mean3707612.162
Median Absolute Deviation (MAD)684776
Skewness28.72846389
Sum1.248353015e+10
Variance2.205419878e+14
MonotocityNot monotonic
2020-12-12T14:45:23.530699image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0140.4%
 
81550820.1%
 
80419420.1%
 
108447520.1%
 
12181121< 0.1%
 
68469961< 0.1%
 
12017811< 0.1%
 
51303031< 0.1%
 
11853901< 0.1%
 
18468421< 0.1%
 
8630261< 0.1%
 
25345921< 0.1%
 
12672751< 0.1%
 
51346071< 0.1%
 
6827821< 0.1%
 
8624411< 0.1%
 
13742991< 0.1%
 
4642681< 0.1%
 
7932111< 0.1%
 
4314931< 0.1%
 
13982891< 0.1%
 
17423511< 0.1%
 
24033471< 0.1%
 
13329131< 0.1%
 
148561981< 0.1%
 
Other values (3326)332698.5%
 
(Missing)90.3%
 
ValueCountFrequency (%) 
-1154171< 0.1%
 
0140.4%
 
31< 0.1%
 
61381< 0.1%
 
113701< 0.1%
 
134091< 0.1%
 
167651< 0.1%
 
168091< 0.1%
 
241051< 0.1%
 
263651< 0.1%
 
ValueCountFrequency (%) 
6570743891< 0.1%
 
2745324951< 0.1%
 
1686836021< 0.1%
 
1504762831< 0.1%
 
1393548281< 0.1%
 
1156412101< 0.1%
 
900604971< 0.1%
 
878518621< 0.1%
 
749173521< 0.1%
 
686368221< 0.1%
 

NaturalGas(therms)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2109
Distinct (%)62.6%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean13685.04538
Minimum0
Maximum2979090
Zeros1258
Zeros (%)37.3%
Memory size26.5 KiB
2020-12-12T14:45:23.613271image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3237.537598
Q311890.33496
95-th percentile49023.72422
Maximum2979090
Range2979090
Interquartile range (IQR)11890.33496

Descriptive statistics

Standard deviation67097.8083
Coefficient of variation (CV)4.903002252
Kurtosis1201.032447
Mean13685.04538
Median Absolute Deviation (MAD)3237.537598
Skewness30.03889031
Sum46077547.78
Variance4502115878
MonotocityNot monotonic
2020-12-12T14:45:23.691338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0125837.3%
 
2268.46020520.1%
 
5987.1401371< 0.1%
 
12205.678711< 0.1%
 
24782.689451< 0.1%
 
17841.310551< 0.1%
 
25254.291021< 0.1%
 
7804.3203131< 0.1%
 
2674.3999021< 0.1%
 
529756.93751< 0.1%
 
116.08000181< 0.1%
 
53787.144531< 0.1%
 
23806.160161< 0.1%
 
21951.513671< 0.1%
 
6886.7998051< 0.1%
 
34789.640631< 0.1%
 
2747.4699711< 0.1%
 
22536.210941< 0.1%
 
160.10000611< 0.1%
 
197.29000851< 0.1%
 
21057.173831< 0.1%
 
18313.960941< 0.1%
 
5456.270021< 0.1%
 
9984.9794921< 0.1%
 
5663.3701171< 0.1%
 
Other values (2084)208461.7%
 
(Missing)90.3%
 
ValueCountFrequency (%) 
0125837.3%
 
0.3299999541< 0.1%
 
1.5300000911< 0.1%
 
2.1999998091< 0.1%
 
3.3200001721< 0.1%
 
3.759999991< 0.1%
 
7.0809097291< 0.1%
 
7.6388001441< 0.1%
 
8.8299999241< 0.1%
 
9.4699993131< 0.1%
 
ValueCountFrequency (%) 
29790901< 0.1%
 
1381912.3751< 0.1%
 
846680.93751< 0.1%
 
679905.3751< 0.1%
 
667464.251< 0.1%
 
560966.1251< 0.1%
 
546713.93751< 0.1%
 
529756.93751< 0.1%
 
346853.31251< 0.1%
 
328535.1251< 0.1%
 

NaturalGas(kBtu)
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2109
Distinct (%)62.6%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1368504.541
Minimum0
Maximum297909000
Zeros1258
Zeros (%)37.3%
Memory size26.5 KiB
2020-12-12T14:45:23.772908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median323754
Q31189033.5
95-th percentile4902372.3
Maximum297909000
Range297909000
Interquartile range (IQR)1189033.5

Descriptive statistics

Standard deviation6709780.835
Coefficient of variation (CV)4.903002242
Kurtosis1201.032444
Mean1368504.541
Median Absolute Deviation (MAD)323754
Skewness30.03889028
Sum4607754791
Variance4.502115885e+13
MonotocityNot monotonic
2020-12-12T14:45:23.850475image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0125837.3%
 
22684620.1%
 
10676301< 0.1%
 
3467891< 0.1%
 
2256171< 0.1%
 
21353001< 0.1%
 
119891< 0.1%
 
19278011< 0.1%
 
4860441< 0.1%
 
29380701< 0.1%
 
1539401< 0.1%
 
2975181< 0.1%
 
133249521< 0.1%
 
6198151< 0.1%
 
42703851< 0.1%
 
2032651< 0.1%
 
1836351< 0.1%
 
8899501< 0.1%
 
45980161< 0.1%
 
10594981< 0.1%
 
6894951< 0.1%
 
9965441< 0.1%
 
8574341< 0.1%
 
10650361< 0.1%
 
2853581< 0.1%
 
Other values (2084)208461.7%
 
(Missing)90.3%
 
ValueCountFrequency (%) 
0125837.3%
 
331< 0.1%
 
1531< 0.1%
 
2201< 0.1%
 
3321< 0.1%
 
3761< 0.1%
 
7081< 0.1%
 
7641< 0.1%
 
8831< 0.1%
 
9471< 0.1%
 
ValueCountFrequency (%) 
2979090001< 0.1%
 
1381912381< 0.1%
 
846680941< 0.1%
 
679905381< 0.1%
 
667464251< 0.1%
 
560966121< 0.1%
 
546713941< 0.1%
 
529756941< 0.1%
 
346853311< 0.1%
 
328535121< 0.1%
 

DefaultData
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
3263 
True
 
113
ValueCountFrequency (%) 
False326396.7%
 
True1133.3%
 
2020-12-12T14:45:23.905022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Comments
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3376
Missing (%)100.0%
Memory size26.5 KiB

ComplianceStatus
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
Compliant
3211 
Error - Correct Default Data
 
113
Non-Compliant
 
37
Missing Data
 
15
ValueCountFrequency (%) 
Compliant321195.1%
 
Error - Correct Default Data1133.3%
 
Non-Compliant371.1%
 
Missing Data150.4%
 
2020-12-12T14:45:23.950561image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:45:23.992097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:24.047644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length9
Mean length9.693127962
Min length9

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a361711.1%
 
t360211.0%
 
o351110.7%
 
C336110.3%
 
l336110.3%
 
n330010.1%
 
i327810.0%
 
m32489.9%
 
p32489.9%
 
r5651.7%
 
4671.4%
 
D2410.7%
 
e2260.7%
 
-1500.5%
 
E1130.3%
 
c1130.3%
 
f1130.3%
 
u1130.3%
 
N370.1%
 
s300.1%
 
M15< 0.1%
 
g15< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2834086.6%
 
Uppercase Letter376711.5%
 
Space Separator4671.4%
 
Dash Punctuation1500.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C336189.2%
 
D2416.4%
 
E1133.0%
 
N371.0%
 
M150.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a361712.8%
 
t360212.7%
 
o351112.4%
 
l336111.9%
 
n330011.6%
 
i327811.6%
 
m324811.5%
 
p324811.5%
 
r5652.0%
 
e2260.8%
 
c1130.4%
 
f1130.4%
 
u1130.4%
 
s300.1%
 
g150.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
467100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-150100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin3210798.1%
 
Common6171.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a361711.3%
 
t360211.2%
 
o351110.9%
 
C336110.5%
 
l336110.5%
 
n330010.3%
 
i327810.2%
 
m324810.1%
 
p324810.1%
 
r5651.8%
 
D2410.8%
 
e2260.7%
 
E1130.4%
 
c1130.4%
 
f1130.4%
 
u1130.4%
 
N370.1%
 
s300.1%
 
M15< 0.1%
 
g15< 0.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
46775.7%
 
-15024.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII32724100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a361711.1%
 
t360211.0%
 
o351110.7%
 
C336110.3%
 
l336110.3%
 
n330010.1%
 
i327810.0%
 
m32489.9%
 
p32489.9%
 
r5651.7%
 
4671.4%
 
D2410.7%
 
e2260.7%
 
-1500.5%
 
E1130.3%
 
c1130.3%
 
f1130.3%
 
u1130.3%
 
N370.1%
 
s300.1%
 
M15< 0.1%
 
g15< 0.1%
 

Outlier
Categorical

MISSING

Distinct2
Distinct (%)6.2%
Missing3344
Missing (%)99.1%
Memory size26.5 KiB
Low outlier
23 
High outlier
9 
ValueCountFrequency (%) 
Low outlier230.7%
 
High outlier90.3%
 
(Missing)334499.1%
 
2020-12-12T14:45:24.111700image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T14:45:24.150733image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:24.193770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length3
Mean length3.078495261
Min length3

Overview of Unicode Properties

Unique unicode characters15
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n668864.4%
 
a334432.2%
 
o550.5%
 
i410.4%
 
320.3%
 
u320.3%
 
t320.3%
 
l320.3%
 
e320.3%
 
r320.3%
 
L230.2%
 
w230.2%
 
H90.1%
 
g90.1%
 
h90.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter1032999.4%
 
Uppercase Letter320.3%
 
Space Separator320.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n668864.7%
 
a334432.4%
 
o550.5%
 
i410.4%
 
u320.3%
 
t320.3%
 
l320.3%
 
e320.3%
 
r320.3%
 
w230.2%
 
g90.1%
 
h90.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
L2371.9%
 
H928.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
32100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin1036199.7%
 
Common320.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n668864.5%
 
a334432.3%
 
o550.5%
 
i410.4%
 
u320.3%
 
t320.3%
 
l320.3%
 
e320.3%
 
r320.3%
 
L230.2%
 
w230.2%
 
H90.1%
 
g90.1%
 
h90.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
32100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII10393100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n668864.4%
 
a334432.2%
 
o550.5%
 
i410.4%
 
320.3%
 
u320.3%
 
t320.3%
 
l320.3%
 
e320.3%
 
r320.3%
 
L230.2%
 
w230.2%
 
H90.1%
 
g90.1%
 
h90.1%
 

TotalGHGEmissions
Real number (ℝ)

Distinct2818
Distinct (%)83.7%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean119.7239709
Minimum-0.8
Maximum16870.98
Zeros9
Zeros (%)0.3%
Memory size26.5 KiB
2020-12-12T14:45:24.260327image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile3.78
Q19.495
median33.92
Q393.94
95-th percentile392.797
Maximum16870.98
Range16871.78
Interquartile range (IQR)84.445

Descriptive statistics

Standard deviation538.8322265
Coefficient of variation (CV)4.500621074
Kurtosis474.8922233
Mean119.7239709
Median Absolute Deviation (MAD)27.94
Skewness19.48187492
Sum403110.61
Variance290340.1683
MonotocityNot monotonic
2020-12-12T14:45:24.336894image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
090.3%
 
3.9570.2%
 
4.260.2%
 
5.4660.2%
 
4.1550.1%
 
6.1850.1%
 
4.850.1%
 
9.2950.1%
 
4.7650.1%
 
4.4350.1%
 
4.5250.1%
 
5.0750.1%
 
3.5450.1%
 
4.0250.1%
 
6.4150.1%
 
4.7450.1%
 
3.6350.1%
 
49.5840.1%
 
6.8440.1%
 
8.140.1%
 
8.740.1%
 
4.1340.1%
 
4.3540.1%
 
6.6440.1%
 
7.0740.1%
 
Other values (2793)324296.0%
 
(Missing)90.3%
 
ValueCountFrequency (%) 
-0.81< 0.1%
 
090.3%
 
0.091< 0.1%
 
0.121< 0.1%
 
0.171< 0.1%
 
0.311< 0.1%
 
0.41< 0.1%
 
0.51< 0.1%
 
0.631< 0.1%
 
0.681< 0.1%
 
ValueCountFrequency (%) 
16870.981< 0.1%
 
12307.161< 0.1%
 
11140.561< 0.1%
 
10734.571< 0.1%
 
8145.521< 0.1%
 
6330.911< 0.1%
 
4906.331< 0.1%
 
3995.451< 0.1%
 
3768.661< 0.1%
 
3278.111< 0.1%
 

GHGEmissionsIntensity
Real number (ℝ)

Distinct511
Distinct (%)15.2%
Missing9
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.175916246
Minimum-0.02
Maximum34.09
Zeros12
Zeros (%)0.4%
Memory size26.5 KiB
2020-12-12T14:45:24.417963image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-0.02
5-th percentile0.13
Q10.21
median0.61
Q31.37
95-th percentile3.961
Maximum34.09
Range34.11
Interquartile range (IQR)1.16

Descriptive statistics

Standard deviation1.821451788
Coefficient of variation (CV)1.548963878
Kurtosis57.37215629
Mean1.175916246
Median Absolute Deviation (MAD)0.44
Skewness5.593144823
Sum3959.31
Variance3.317686616
MonotocityNot monotonic
2020-12-12T14:45:24.495530image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.15992.9%
 
0.17992.9%
 
0.16962.8%
 
0.18862.5%
 
0.19782.3%
 
0.2702.1%
 
0.13662.0%
 
0.14621.8%
 
0.21601.8%
 
0.22541.6%
 
0.23521.5%
 
0.26391.2%
 
0.28371.1%
 
0.12341.0%
 
0.25320.9%
 
0.24310.9%
 
0.27300.9%
 
0.31250.7%
 
0.11230.7%
 
0.33230.7%
 
0.8230.7%
 
0.32230.7%
 
0.34230.7%
 
0.7210.6%
 
0.72210.6%
 
Other values (486)216064.0%
 
ValueCountFrequency (%) 
-0.021< 0.1%
 
0120.4%
 
0.0140.1%
 
0.0240.1%
 
0.0370.2%
 
0.0490.3%
 
0.0590.3%
 
0.06160.5%
 
0.0780.2%
 
0.0880.2%
 
ValueCountFrequency (%) 
34.091< 0.1%
 
25.711< 0.1%
 
16.991< 0.1%
 
16.931< 0.1%
 
16.911< 0.1%
 
16.381< 0.1%
 
15.421< 0.1%
 
14.941< 0.1%
 
14.891< 0.1%
 
14.321< 0.1%
 

Interactions

2020-12-12T14:44:20.151659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:44:20.213213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:44:20.276767image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:44:20.338320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:44:20.396870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:44:20.460425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:44:20.522979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:44:20.587535image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:44:20.649087image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T14:44:20.776197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:44:20.841753image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-12T14:45:11.042453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.112013image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.176568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.246629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.313687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.382245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.445800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.509855image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.576913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.643970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.706024image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.768578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.832133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.894686image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:11.964246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.026800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.094358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.161416image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.227973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.295031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.362089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.428646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.497205image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.562761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.623814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.690371image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.756428image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.824987image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.890043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:12.960103image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.025660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.095720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.162778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.229836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.293890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.357945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.424503image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.491060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.553613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.615667image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.678221image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.740775image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.809834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.872388image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:13.939946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:14.006003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:14.072060image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:14.140619image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T14:45:24.597618image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T14:45:24.821310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T14:45:25.045003image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T14:45:25.279705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-12T14:45:14.374320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:15.254578image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:15.607381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T14:45:15.985707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

OSEBuildingIDDataYearBuildingTypePrimaryPropertyTypePropertyNameAddressCityStateZipCodeTaxParcelIdentificationNumberCouncilDistrictCodeNeighborhoodLatitudeLongitudeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)ListOfAllPropertyUseTypesLargestPropertyUseTypeLargestPropertyUseTypeGFASecondLargestPropertyUseTypeSecondLargestPropertyUseTypeGFAThirdLargestPropertyUseTypeThirdLargestPropertyUseTypeGFAYearsENERGYSTARCertifiedENERGYSTARScoreSiteEUI(kBtu/sf)SiteEUIWN(kBtu/sf)SourceEUI(kBtu/sf)SourceEUIWN(kBtu/sf)SiteEnergyUse(kBtu)SiteEnergyUseWN(kBtu)SteamUse(kBtu)Electricity(kWh)Electricity(kBtu)NaturalGas(therms)NaturalGas(kBtu)DefaultDataCommentsComplianceStatusOutlierTotalGHGEmissionsGHGEmissionsIntensity
012016NonResidentialHotelMayflower park hotel405 Olive waySeattleWA98101.006590000307DOWNTOWN47.61220-122.3379919271.01288434088434HotelHotel88434.0NaNNaNNaNNaNNaN60.081.69999784.300003182.500000189.0000007226362.57456910.02.003882e+061.156514e+063946027.012764.5293001276453.0FalseNaNCompliantNaN249.982.83
122016NonResidentialHotelParamount Hotel724 Pine streetSeattleWA98101.006590002207DOWNTOWN47.61317-122.3339319961.0111035661506488502Hotel, Parking, RestaurantHotel83880.0Parking15064.0Restaurant4622.0NaN61.094.80000397.900002176.100006179.3999948387933.08664479.00.000000e+009.504252e+053242851.051450.8164105145082.0FalseNaNCompliantNaN295.862.86
232016NonResidentialHotel5673-The Westin Seattle1900 5th AvenueSeattleWA98101.006590004757DOWNTOWN47.61393-122.3381019691.041956110196718759392HotelHotel756493.0NaNNaNNaNNaNNaN43.096.00000097.699997241.899994244.10000672587024.073937112.02.156655e+071.451544e+0749526664.014938.0000001493800.0FalseNaNCompliantNaN2089.282.19
352016NonResidentialHotelHOTEL MAX620 STEWART STSeattleWA98101.006590006407DOWNTOWN47.61412-122.3366419261.01061320061320HotelHotel61320.0NaNNaNNaNNaNNaN56.0110.800003113.300003216.199997224.0000006794584.06946800.52.214446e+068.115253e+052768924.018112.1308601811213.0FalseNaNCompliantNaN286.434.67
482016NonResidentialHotelWARWICK SEATTLE HOTEL (ID8)401 LENORA STSeattleWA98121.006590009707DOWNTOWN47.61375-122.3404719801.01817558062000113580Hotel, Parking, Swimming PoolHotel123445.0Parking68009.0Swimming Pool0.0NaN75.0114.800003118.699997211.399994215.60000614172606.014656503.00.000000e+001.573449e+065368607.088039.9843808803998.0FalseNaNCompliantNaN505.012.88
592016Nonresidential COSOtherWest Precinct810 Virginia StSeattleWA98101.006600005607DOWNTOWN47.61623-122.3365719991.02972883719860090Police StationPolice Station88830.0NaNNaNNaNNaNNaNNaN136.100006141.600006316.299988320.50000012086616.012581712.00.000000e+002.160444e+067371434.047151.8164104715182.0FalseNaNCompliantNaN301.813.10
6102016NonResidentialHotelCamlin1619 9th AvenueSeattleWA98101.006600008257DOWNTOWN47.61390-122.3328319261.01183008083008HotelHotel81352.0NaNNaNNaNNaNNaN27.070.80000374.500000146.600006154.6999975758795.06062767.50.000000e+008.239199e+052811215.029475.8007802947580.0FalseNaNCompliantNaN176.142.12
7112016NonResidentialOtherParamount Theatre911 Pine StSeattleWA98101.006600009557DOWNTOWN47.61327-122.3313619261.081027610102761Other - Entertainment/Public AssemblyOther - Entertainment/Public Assembly102761.0NaNNaNNaNNaNNaNNaN61.29999968.800003141.699997152.3000036298131.57067881.52.276286e+061.065843e+063636655.03851.890137385189.0FalseNaNCompliantNaN221.512.16
8122016NonResidentialHotel311wh-Pioneer Square612 2nd AveSeattleWA98104.009390000807DOWNTOWN47.60294-122.3326319041.0151639840163984HotelHotel163984.0NaNNaNNaNNaNNaN43.083.69999786.599998180.899994187.19999713723820.014194054.00.000000e+002.138898e+067297919.064259.0000006425900.0FalseNaNCompliantNaN392.162.39
9132016Multifamily MR (5-9)Mid-Rise MultifamilyLyon Building607 - 3rd Ave.SeattleWA98104.009390001057DOWNTOWN47.60284-122.3318419101.0663712149662216Multifamily HousingMultifamily Housing56132.0NaNNaNNaNNaNNaN1.081.50000085.599998182.699997187.3999944573777.04807679.51.039735e+067.420912e+052532015.010020.2597701002026.0FalseNaNCompliantNaN151.122.37

Last rows

OSEBuildingIDDataYearBuildingTypePrimaryPropertyTypePropertyNameAddressCityStateZipCodeTaxParcelIdentificationNumberCouncilDistrictCodeNeighborhoodLatitudeLongitudeYearBuiltNumberofBuildingsNumberofFloorsPropertyGFATotalPropertyGFAParkingPropertyGFABuilding(s)ListOfAllPropertyUseTypesLargestPropertyUseTypeLargestPropertyUseTypeGFASecondLargestPropertyUseTypeSecondLargestPropertyUseTypeGFAThirdLargestPropertyUseTypeThirdLargestPropertyUseTypeGFAYearsENERGYSTARCertifiedENERGYSTARScoreSiteEUI(kBtu/sf)SiteEUIWN(kBtu/sf)SourceEUI(kBtu/sf)SourceEUIWN(kBtu/sf)SiteEnergyUse(kBtu)SiteEnergyUseWN(kBtu)SteamUse(kBtu)Electricity(kWh)Electricity(kBtu)NaturalGas(therms)NaturalGas(kBtu)DefaultDataCommentsComplianceStatusOutlierTotalGHGEmissionsGHGEmissionsIntensity
3366502102016Nonresidential COSOfficeCentral West HQ / Brown Bear1403 w howeSeattleWANaN24250391377MAGNOLIA / QUEEN ANNE47.63572-122.3752519521.0113661013661OfficeOffice13661.0NaNNaNNaNNaNNaN75.036.79999940.900002115.500000128.3999945.026677e+055.585251e+050.0147323.500005.026678e+050.0000000.000000e+00TrueNaNError - Correct Default DataNaN3.500.26
3367502122016Nonresidential COSOtherConservatory Campus1400 E Galer StSeattleWANaN29250490873EAST47.63228-122.3157419121.0123445023445Other - RecreationOther - Recreation23445.0NaNNaNNaNNaNNaNNaN254.899994286.500000380.100006413.2000125.976246e+066.716330e+060.0369539.812501.260870e+0647153.7578104.715376e+06FalseNaNCompliantNaN259.2211.06
3368502192016Nonresidential COSMixed Use PropertyGarfield Community Center2323 East Cherry StSeattleWANaN75448002453CENTRAL47.60775-122.3022519941.0120050020050Fitness Center/Health Club/Gym, Office, Other - Recreation, Other - Technology/ScienceOther - Recreation8108.0Fitness Center/Health Club/Gym7726.0Office3779.0NaNNaN90.40000299.400002175.199997184.6000061.813404e+061.993137e+060.0225513.796907.694531e+0510439.5107401.043951e+06FalseNaNCompliantNaN60.813.03
3369502202016Nonresidential COSOfficeGenesee/SC SE HQ4420 S GeneseeSeattleWANaN41543005852SOUTHEAST47.56440-122.2781319601.0115398015398OfficeOffice15398.0NaNNaNNaNNaNNaN93.025.20000126.90000064.09999866.6999973.878100e+054.141724e+050.081341.398442.775369e+051102.7299801.102730e+05TrueNaNError - Correct Default DataNaN7.790.51
3370502212016Nonresidential COSOtherHigh Point Community Center6920 34th Ave SWSeattleWANaN25240390591DELRIDGE NEIGHBORHOODS47.54067-122.3744119821.0118261018261Other - RecreationOther - Recreation18261.0NaNNaNNaNNaNNaNNaN51.00000056.200001126.000000136.6000069.320821e+051.025432e+060.0185334.703106.323620e+052997.1999512.997200e+05FalseNaNCompliantNaN20.331.11
3371502222016Nonresidential COSOfficeHorticulture building1600 S Dakota StSeattleWANaN16240490802GREATER DUWAMISH47.56722-122.3115419901.0112294012294OfficeOffice12294.0NaNNaNNaNNaNNaN46.069.09999876.699997161.699997176.1000068.497457e+059.430032e+050.0153655.000005.242709e+053254.7502443.254750e+05TrueNaNError - Correct Default DataNaN20.941.70
3372502232016Nonresidential COSOtherInternational district/Chinatown CC719 8th Ave SSeattleWANaN35583000002DOWNTOWN47.59625-122.3228320041.0116000016000Other - RecreationOther - Recreation16000.0NaNNaNNaNNaNNaNNaN59.40000265.900002114.199997118.9000019.502762e+051.053706e+060.0116221.000003.965461e+055537.2998055.537300e+05FalseNaNCompliantNaN32.172.01
3373502242016Nonresidential COSOtherQueen Anne Pool1920 1st Ave WSeattleWANaN17945011507MAGNOLIA / QUEEN ANNE47.63644-122.3578419741.0113157013157Fitness Center/Health Club/Gym, Other - Recreation, Swimming PoolOther - Recreation7583.0Fitness Center/Health Club/Gym5574.0Swimming Pool0.0NaNNaN438.200012460.100006744.799988767.7999885.765898e+066.053764e+060.0525251.687501.792159e+0639737.3906303.973739e+06FalseNaNCompliantNaN223.5416.99
3374502252016Nonresidential COSMixed Use PropertySouth Park Community Center8319 8th Ave SSeattleWANaN78836031551GREATER DUWAMISH47.52832-122.3243119891.0114101014101Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation6601.0Fitness Center/Health Club/Gym6501.0Pre-school/Daycare484.0NaNNaN51.00000055.500000105.300003110.8000037.194712e+057.828413e+050.0102248.000003.488702e+053706.0100103.706010e+05FalseNaNCompliantNaN22.111.57
3375502262016Nonresidential COSMixed Use PropertyVan Asselt Community Center2820 S Myrtle StSeattleWANaN78570020302GREATER DUWAMISH47.53939-122.2953619381.0118258018258Fitness Center/Health Club/Gym, Food Service, Office, Other - Recreation, Pre-school/DaycareOther - Recreation8271.0Fitness Center/Health Club/Gym8000.0Pre-school/Daycare1108.0NaNNaN63.09999870.900002115.800003123.9000011.152896e+061.293722e+060.0126774.398404.325542e+057203.4199227.203420e+05FalseNaNCompliantNaN41.272.26